Deep Learning-Based Multiclass Framework for Real-Time Melasma Severity Classification: Clinical Image Analysis and Model Interpretability Evaluation by Zhang J, Jiang Q, Chen Q, Hu B, Chen L

“…Future work will integrate multimodal data for more comprehensive assessment.Keywords: melasma, deep learning, convolutional neural networks, MASI, clinical decision support…”
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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. …”
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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

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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. …”
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ANALYSIS OF ECONOMIC THOUGHT IN THE CONTEXT OF PREREQUISITES FOR THE DEVELOPMENT OF CLUSTER FORMATIONS IN THE WORKS OF FOREIGN SCIENTISTS by Anastasiia O. Vasylchenko, Olena V. Dymchenko

“…The authors present the evolution of the formation and development of the cluster approach in Economics in Western Economic Thought, give the author’s definition of the concept of “cluster” and describe the common features characteristic of clusters, provide reasoned conclusions.…”
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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

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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. …”
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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). …”
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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). …”
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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. …”
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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. …”
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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. …”
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Neuro-Evolution of Augmenting Topologies for Dynamic Scheduling of Flexible Job Shop Problem by Jian Huang, Yarong Chen, Jabir Mumtaz, Liuyan Zhong

“…Employing the entropy weight method, a fitness function for multiobjective optimization is formulated, facilitating the enhancement of the neural network’s structural and nodal parameters through genetic algorithms. …”
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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. …”
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Predicting the Evolution of Lung Squamous Cell Carcinoma In Situ Using Computational Pathology by Alon Vigdorovits, Gheorghe-Emilian Olteanu, Ovidiu Tica, Andrei Pascalau, Monica Boros, Ovidiu Pop

“…We used this dataset to train two models: a pathomics-based ridge classifier trained on 80 principal components derived from almost 2000 extracted features and a deep convolutional neural network with a modified ResNet18 architecture. …”
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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. …”
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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. …”
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Infant Type Ia Supernovae from the KMTNet. I. Multicolor Evolution and Populations by Yuan Qi Ni, Dae-Sik Moon, Maria R. Drout, Youngdae Lee, Patrick Sandoval, Jeehye Shin, Hong Soo Park, Sang Chul Kim, Kyuseok Oh

“…We conduct a systematic analysis of the early multiband light curves and colors of 19 Type Ia supernovae (SNe) from the Korea Microlensing Telescope Network SN Program, including 16 previously unpublished events. …”
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Evolution of HD-ZIP transcription factors and their function in cabbage leafy head formation by Ju Zhang, Ju Zhang, Can Chen, Can Chen, Qihang Yang, Jie Xu, Zizhuo Han, Wei Ma, Xiaomeng Zhang, Kedong Xu, Jianjun Zhao, Xueping Chen

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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. …”
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