Low-voltage/temperature double responsive N-isopropylacrylamide based shape-changing double network hydrogel with enhanced mechanical properties for controlled drug release and its... by Qiang Ma, Linyan Wang, Guohe Xu, Mengru Wang, Jiale Li, Zetao He

“…A low-voltage/temperature double responsive shape-changing double network hydrogel with enhanced mechanical properties based on N-isopropylacrylamide (NIPAAm) and 5-acrylamido-1,10-phenanthroline bis (1,10-phenanthroline) iron (Ⅱ) (Fe(phen)3) was synthesized. …”
Get full text

Optimizing Remote Sensing Image Retrieval Through a Hybrid Methodology by Sujata Alegavi, Raghvendra Sedamkar

“…Given the complexity of remote sensing images, feature extraction occurs at two levels. Low-level features are extracted using the modified Multiscale Multiangle Completed Local Binary Pattern (MSMA-CLBP) algorithm to capture local contexture features, while high-level features are obtained through a hybrid CNN structure combining pretrained networks (Alexnet, Caffenet, VGG-S, VGG-M, VGG-F, VGG-VDD-16, VGG-VDD-19) and a fully connected dense network. …”
Get full text

Enhancing PM_2.5 Air Pollution Prediction Performance by Optimizing the Echo State Network (ESN) Deep Learning Model Using New Metaheuristic Algorithms by Iman Zandi, Ali Jafari, Aynaz Lotfata

“…Boruta-XGBoost highlighted PM₁₀ as the most important feature. Wavelet transform was then applied to extract 40 features to enhance prediction accuracy. …”
Get full text

Development of an XGBoost-based prediction model for wound recurrence risk in diabetic foot ulcer patients treated with antibiotic-loaded bone cement by Yi Zhang, Yi Zhang, Xingyu Sun, Cheng Cheng, Nianzong Hou, Nianzong Hou, Shiliang Han, Xin Tang

“…Artificial neural network, support vector machine, and XGBoost prediction models were built according to the selected optimal features. …”
Get full text

Evaluating Wind Speed Forecasting Models: A Comparative Study of CNN, DAN2, Random Forest and XGBOOST in Diverse South African Weather Conditions by Fhulufhelo Walter Mugware, Caston Sigauke, Thakhani Ravele

Get full text

Urban rail transit resilience under different operation schemes: A percolation-based approach by Tianlei Zhu, Xin Yang, Yun Wei, Anthony Chen, Jianjun Wu

“…Our model accounts for the state evolution of different hierarchical structures within the network and identifies nonlinear features. …”
Get full text

An ensemble of deep representation learning with metaheuristic optimisation algorithm for critical health monitoring using internet of medical things by Mai Alduailij

“…The classification process utilizes ensemble models, including the Temporal Convolutional Network (TCN), the Attention-based Bidirectional Gated Recurrent Unit (A-BiGRU), and the Hybrid Deep Belief Network (HDBN) techniques. …”
Get full text

Research on YOLOv5 Oracle Recognition Algorithm Based on Multi-Module Fusion by Xinhang Zhang, Zhenhua Ma, Yaru Zhang, Huiying Ru

“…The recognition of oracle bone script is of significant importance for understanding the evolution of Chinese characters, their morphological features, and semantic changes. …”
Get full text

Hypergraph-Driven High-Order Knowledge Tracing with a Dual-Gated Dynamic Mechanism by Fanglan Ma, Changsheng Zhu, Peng Lei

Get full text

Generative Artificial Intelligence for Hyperspectral Sensor Data: A Review by Diaa Addeen Abuhani, Imran Zualkernan, Raghad Aldamani, Mohamed Alshafai

“…GAI methods address the inherent challenges of HSI data, such as high dimensionality, noise, and the need to preserve spectral-spatial correlations, rendering them indispensable for modern HSI analysis. Generative neural networks, including generative adversarial networks and denoising diffusion probabilistic models, are highlighted for their superior performance in classification, segmentation, and object identification tasks, often surpassing traditional approaches, such as U-Nets, autoencoders, and deep convolutional neural networks. …”
Get full text

Using Machine Learning Algorithms in Intrusion Detection Systems: A Review by Mazin S. Mohammed, Hasanien Ali Talib

“… Intrusion Detection Systems (IDS) are essential for identifying and mitigating security threats in Internet of Things (IoT) networks. This paper explores the unique challenges of IoT environments and presents machine learning (ML) algorithms as powerful solutions for IoT-IDS, encompassing supervised, unsupervised, and semi-supervised learning. …”
Get full text

TEBS: Temperature–Emissivity–Driven band selection for thermal infrared hyperspectral image classification with structured State-Space model and gated attention by Enyu Zhao, Nianxin Qu, Yulei Wang, Caixia Gao, Jian Zeng

Get full text

Access control relationship prediction method based on GNN dual source learning by Dibin SHAN, Xuehui DU, Wenjuan WANG, Aodi LIU, Na WANG

“…With the rapid development and wide application of big data technology, users’ unauthorized access to resources becomes one of the main problems that restrict the secure sharing and controlled access to big data resources.The ReBAC (Relationship-Based Access Control) model uses the relationship between entities to formulate access control rules, which enhances the logical expression of policies and realizes dynamic access control.However, It still faces the problems of missing entity relationship data and complex relationship paths of rules.To overcome these problems, a link prediction model LPMDLG based on GNN dual-source learning was proposed to transform the big data entity-relationship prediction problem into a link prediction problem with directed multiple graphs.A topology learning method based on directed enclosing subgraphs was designed in this modeled.And a directed dual-radius node labeling algorithm was proposed to learn the topological structure features of nodes and subgraphs from entity relationship graphs through three segments, including directed enclosing subgraph extraction, subgraph node labeling calculation and topological structure feature learning.A node embedding feature learning method based on directed neighbor subgraph was proposed, which incorporated elements such as attention coefficients and relationship types, and learned its node embedding features through the sessions of directed neighbor subgraph extraction and node embedding feature learning.A two-source fusion scoring network was designed to jointly calculate the edge scores by topology and node embedding to obtain the link prediction results of entity-relationship graphs.The experiment results of link prediction show that the proposed model obtains better prediction results under the evaluation metrics of AUC-PR, MRR and Hits@N compared with the baseline models such as R-GCN, SEAL, GraIL and TACT.The ablation experiment results illustrate that the model’s dual-source learning scheme outperforms the link prediction effect of a single scheme.The rule matching experiment results verify that the model achieves automatic authorization of some entities and compression of the relational path of rules.The model effectively improves the effect of link prediction and it can meet the demand of big data access control relationship prediction.…”
Get full text

Automated crowdturfing attack in Chinese user reviews by Li’na WANG, Xiaodong GUO, Run WANG

“…The text-oriented automated crowdturfing attack has a series of features such as low attack cost and strong concealment.This kind of attack can automatically generate a large number of fake reviews,with harmful effect on the healthy development of the user review community.In recent years,researchers have found that text-oriented crowdturfing attacks for the English review community,but there was few research work on automated crowdsourcing attacks in the Chinese review community.A Chinese character embedding LSTM model was proposed to automatically generate Chinese reviews with the aim of antomated crowdturfing attacks,which model trained by a combination with Chinese character embedding network,LSTM network and softmax dense network,and a temperature parameter T was designed to construct the attack model.In the experiment,more than 50 000 real user reviews were crawled from Taobao's online review platform to verify the effectiveness of the attack method.Experimental results show that the generated fake reviews can effectively fool linguistics-based classification detection approach and texts plagiarism detection approach.Besides,the massive manually evaluation experiments also demonstrate that the generated reviews with the proposed attack approach perform well in reality and diversity.…”
Get full text

Satellite retrievals of total phosphorus in Taihu Lake using Sentinel-2 images and an optimized XGBoost model by Jiayu Cui, Shiqiang Wu, Jiangyu Dai, Wanyun Xue, Yue Zhang, Jiayi You, Xueyan Lv, Xuan Yang

“…The model employed single bands and band ratios at various resolutions as input features, optimized model parameters through Bayesian optimization, and incrementally introduced variables with substantial contributions to evaluate and determine the optimal feature combination. …”
Get full text

Machine learning prediction of permeability distribution in the X field Malay Basin using elastic properties by Zaky Ahmad Riyadi, John Oluwadamilola Olutoki, Maman Hermana, Abdul Halim Abdul Latif, Ida Bagus Suananda Yogi, Said Jadid A. Kadir

“…Several ensemble-based models, including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LightBoost), Categorical Gradient Boosting (CatBoost), Bagging Regressor, Random Forest and Stacking, were evaluated for predictive performance, along with Multi-Layered Perceptron Neural Network algorithms. …”
Get full text

Aircraft Multi-stage Altitude Prediction Under Satellite Signal Loss by Mengchan HUANG, Qiang MIAO

“…Three subsets of data corresponding to different flight phases are utilized for altitude prediction, and the performance is compared to commonly used convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory networks (LSTM), gated recurrent units (GRU), TCN, and the LTCA–TCN algorithm proposed in this study. …”
Get full text

Application of a Multi‐Layer Artificial Neural Network in a 3‐D Global Electron Density Model Using the Long‐Term Observations of COSMIC, Fengyun‐3C, and Digisonde by Wang Li, Dongsheng Zhao, Changyong He, Yi Shen, Andong Hu, Kefei Zhang

“…However, it is still a challenging mission to develop a model with high predictability that captures the horizontal‐vertical features of ionospheric electrodynamics. In this study, multiple observations during 2005–2019 from space‐borne global navigation satellite system (GNSS) radio occultation (RO) systems (COSMIC and FY‐3C) and the Digisonde Global Ionosphere Radio Observatory are utilized to develop a completely global ionospheric three‐dimensional electron density model based on an artificial neural network, namely ANN‐TDD. …”
Get full text

MATHEMATICAL MODEL OF THE LOAD BALANCING SYSTEM OF DPC SERVER CLUSTERS UNDER FRACTAL LOAD CONDITIONS by V. P. Mochalov, N. Yu. Bratchenko, I. S. Palkanov, E. V. Aliev

“…The model is built taking into account the features of the network traffic of modern infocommunication networks, characterized by self-similarity properties, and each type of traffic (HTTP/TCP, HTTPS. …”
Get full text

Deep Learning Architectures for Single-Label and Multi-Label Surgical Tool Classification in Minimally Invasive Surgeries by Hisham ElMoaqet, Hamzeh Qaddoura, Mutaz Ryalat, Natheer Almtireen, Tamer Abdulbaki Alshirbaji, Nour Aldeen Jalal, Thomas Neumuth, Knut Moeller

“…This study proposes a novel deep learning approach for surgical tool classification based on combining convolutional neural networks (CNNs), Feature Fusion Modules (FFMs), Squeeze-and-Excitation (SE) networks, and Bidirectional long-short term memory (BiLSTM) networks to capture both spatial and temporal features in laparoscopic surgical videos. …”
Get full text