Neural networks in oncourology by M. P. Korchagin, A. V. Govorov, A. O. Vasilyev, I. O. Gritskov, D. Yu. Pushkar

“…Accurate and early diagnosis of malignancies is a key challenge in oncology. Neural networks can analyse a wide range of medical data and identify relationships between qualitative and quantitative features. …”
Get full text

GINGIVAL TISSUE MICROCIRCULATION FEATURES IN PATIENTS WITH INCISOR CROWDING by D. A. Domenyuk, A. G. Karslieva, E. G. Vedeshina, S. V. Dmitrienko, A. S. Kochkonyan, Yu. S. Arutyunyan

Get full text

TDMSANet: A Tri-Dimensional Multi-Head Self-Attention Network for Improved Crop Classification from Multitemporal Fine-Resolution Remotely Sensed Images by Jian Li, Xuhui Tang, Jian Lu, Hongkun Fu, Miao Zhang, Jujian Huang, Ce Zhang, Huapeng Li

“…In addition, the positional encoding was adopted by both temporal and spatial submodules to learn the sequence relationships between the features in a feature sequence. The proposed TDMSANet was evaluated on two sites utilizing FSR SAR (UAVSAR) and optical (Rapid Eye) images, respectively. …”
Get full text

Advancing tourism demand forecasting in Sri Lanka: evaluating the performance of machine learning models and the impact of social media data integration by Isuru Udayangani Hewapathirana

Get full text

Filamentary Convolution for SLI: A Brain-Inspired Approach with High Efficiency by Boyuan Zhang, Xibang Yang, Tong Xie, Shuyuan Zhu, Bing Zeng

“…While the short-time Fourier transform (STFT) generates time–frequency acoustic features (TFAF) for deep learning networks (DLNs), rectangular convolution kernels cause frequency mixing and aliasing, degrading feature extraction. …”
Get full text

Enhanced Feature Selection via Hierarchical Concept Modeling by Jarunee Saelee, Patsita Wetchapram, Apirat Wanichsombat, Arthit Intarasit, Jirapond Muangprathub, Laor Boongasame, Boonyarit Choopradit

“…The presented methods are evaluated based on all learned attributes with 10 datasets from the UCI Machine Learning Repository by using three classification algorithms, namely decision trees, support vector machines (SVM), and artificial neural networks (ANN). …”
Get full text

An Improved Convolutional Neural Networks: Quantum Pseudo-Transposed Convolutional Neural Networks by Li Hai, Chen Liang, Hao Yaming, Yu Wenli, Shi Fengquan

“…Building on the operational principles of classical transposed convolutional neural networks (CNNs), we introduce a novel quantum variant: the Quantum Pseudo-Transposed Convolutional Neural Network (QPTCNN). …”
Get full text

An optimization model of computer network security based on GABP neural network algorithm by Jiangang Wang, Xiaoyan Wang

“…Objective This research proposed the investigation of the Genetic Algorithm with Back Propagation Neural Network (GA-BPNN) model for computer network safety. …”
Get full text

Learning Frequency-Aware Spatial Attention by Reconstructing Images With Different Frequency Responses by Keisuke Sano, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi

“…Through comprehensive quantitative and qualitative evaluations on both general and fine-grained image classification datasets, we demonstrate that our method achieves higher accuracy compared to the Attention Branch Network, a representative spatial attention method. …”
Get full text

Developing the new diagnostic model by integrating bioinformatics and machine learning for osteoarthritis by Jian Du, Tian Zhou, Wei Zhang, Wei Peng

“…Differentially expressed genes (DEGs) associated with OA were identified through utilization of the Limma package and weighted gene co-expression network analysis (WGCNA). Subsequently, protein-protein interaction (PPI) network analysis and machine learning were employed to identify the most relevant potential feature genes of OA, and ANN diagnostic model and receiver operating characteristic (ROC) curve were constructed to evaluate the diagnostic performance of the model. …”
Get full text

Deep-m6Am: a deep learning model for identifying N6, 2′-O-Dimethyladenosine (m6Am) sites using hybrid features by Islam Uddin, Salman A. AlQahtani, Sumaiya Noor, Salman Khan

“…Finally, a multilayer deep neural network (DNN) is used as a classification algorithm for identifying m6Am sites. …”
Get full text

A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery by Zhen-qi LIAO, Yu-long DAI, Han WANG, Quirine M. KETTERINGS, Jun-sheng LU, Fu-cang ZHANG, Zhi-jun LI, Jun-liang FAN

“…A total of 23 spectral features (SFs; five original spectrum bands, 17 vegetation indices and the gray scale of the RGB image) and eight texture features (TFs; contrast, entropy, variance, mean, homogeneity, dissimilarity, second moment, and correlation) were selected as inputs for the models. …”
Get full text

Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network by Haohua Zheng, Jianchen Zhang, Heying Li, Guangxia Wang, Jianzhong Guo, Jiayao Wang

“…To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where the feature masking method is initially utilized to assess the significance of road features. …”
Get full text

A survey on network simulators in three-dimensional wireless ad hoc and sensor networks by SeokYoon Kang, Monther Aldwairi, Ki-Il Kim

“…As steady research in wireless ad hoc and sensor networks is going on, performance evaluation through relevant network simulator becomes indispensable procedure to demonstrate superiority to comparative schemes and suitability in most literatures. …”
Get full text

Deep Learning-Based Seedling Row Detection and Localization Using High-Resolution UAV Imagery for Rice Transplanter Operation Quality Evaluation by Yangfan Luo, Jiuxiang Dai, Shenye Shi, Yuanjun Xu, Wenqi Zou, Haojia Zhang, Xiaonan Yang, Zuoxi Zhao, Yuanhong Li

“…We have introduced convolutional block attention module (CBAM) and attention gate (AG) modules on the basis of the original UNet network, which can merge multiple feature maps or information flows together, helping the model better select key areas or features of seedling rows in the image, thereby improving the understanding of image content and task execution performance. …”
Get full text

Integrating particle swarm optimization with backtracking search optimization feature extraction with two-dimensional convolutional neural network and attention-based stacked bidir... by Jyotirmayee Rautaray, Sangram Panigrahi, Ajit Kumar Nayak

“…This study introduces an improvised Particle Swarm Optimization with Backtracking Search Optimization (PSOBSA) designed for feature extraction. For classification purpose, it recommends two-dimensional convolutional neural network (2D CNN) along with an attention-based stacked bidirectional long short-term memory (ABS-BiLSTM) model to generate new summarized sentences by analyzing entire sentences. …”
Get full text

Hybrid Approach for WDM Network Restoration: Deep Reinforcement Learning and Graph Neural Networks by Isaac Ampratwum, Amiya Nayak

“…The proposed method leverages the decision-making capabilities of DRL and the graph-structured learning potential of GNN to dynamically adapt to network disruptions. By modeling network topology as a graph, the GNN extracts structural features, while the DRL agent intelligently selects restoration paths, balancing network load and minimizing restoration time. …”
Get full text

Deep Complex Gated Recurrent Networks-Based IoT Network Intrusion Detection Systems by Engy El-Shafeiy, Walaa M. Elsayed, Haitham Elwahsh, Maazen Alsabaan, Mohamed I. Ibrahem, Gamal Farouk Elhady

“…Furthermore, DCGR_IoT harnesses complex gated recurrent networks (CGRNs) to construct multidimensional feature subsets, enabling a more detailed spatial representation of network traffic and facilitating the extraction of critical features that are essential for intrusion detection. …”
Get full text

Learning Feature Fusion in Deep Learning-Based Object Detector by Ehtesham Hassan, Yasser Khalil, Imtiaz Ahmad

“…Our hypothesis is to reinforce these features with handcrafted features by learning the optimal fusion during network training. …”
Get full text

ACFM: Adaptive Channel Feature Matching for Pedestrian Re-Identification by Zhengcai Lu, Zhengwei Tian

“…The multi-branch structure is designed to handle both global and local features, enabling the network to comprehensively capture and integrate image information. …”
Get full text