A Lightweight Breast Cancer Mass Classification Model Utilizing Simplified Swarm Optimization and Knowledge Distillation by Wei-Chang Yeh, Wei-Chung Shia, Yun-Ting Hsu, Chun-Hui Huang, Yong-Shiuan Lee

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Inverse design of face-like 3D surfaces via bi-material 4D printing and shape morphing by Yi-Hung Chiu, Yu-Ting Huang, Mao-Chuan Chen, Yi-Xian Xu, Yu-Chen Yen, Jia-Yang Juang

“…Shape morphing from two-dimensional (2D) to three-dimensional (3D) structures enables novel fabrication approaches beyond conventional methods but often requires costly tools. Here, we demonstrate a low-cost, bi-material four-dimensional (4D) printing approach to fabricate human face-like 3D gridshells from 2D grids. …”
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Enhancing Steganography Detection with AI: Fine-Tuning a Deep Residual Network for Spread Spectrum Image Steganography by Oleksandr Kuznetsov, Emanuele Frontoni, Kyrylo Chernov, Kateryna Kuznetsova, Ruslan Shevchuk, Mikolaj Karpinski

“…This paper presents an extensive investigation into the application of artificial intelligence, specifically Convolutional Neural Networks (CNNs), in image steganography detection. …”
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A Complete Transfer Learning-Based Pipeline for Discriminating Between Select Pathogenic Yeasts from Microscopy Photographs by Ryan A. Parker, Danielle S. Hannagan, Jan H. Strydom, Christopher J. Boon, Jessica Fussell, Chelbie A. Mitchell, Katie L. Moerschel, Aura G. Valter-Franco, Christopher T. Cornelison

“…This study presents a convolutional neural network (CNN)-based approach for classifying pathogenic yeast species from microscopy images. …”
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Enhanced Conformer-Based Speech Recognition via Model Fusion and Adaptive Decoding with Dynamic Rescoring by Junhao Geng, Dongyao Jia, Zihao He, Nengkai Wu, Ziqi Li

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Transformers for Vision: A Survey on Innovative Methods for Computer Vision by Balamurugan Palanisamy, Vikas Hassija, Arpita Chatterjee, Arpita Mandal, Debanshi Chakraborty, Amit Pandey, G. S. S. Chalapathi, Dhruv Kumar

“…Transformers have emerged as a groundbreaking architecture in the field of computer vision, offering a compelling alternative to traditional convolutional neural networks (CNNs) by enabling the modeling of long-range dependencies and global context through self-attention mechanisms. …”
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Management system and optimal control for three-dimensional visualization and maintenance of thermal power plant by Zhiqiang Feng, Qiuxiang Liang, Mingyi Wei, Lei Li, Youzhu Bu, Yanqing Xin

“…The purpose of this study is to build an advanced three-dimensional (3D) visualization and maintenance system suitable for thermal power plants, and to optimize it with the technology of convolutional neural network (CNN). Firstly, literature research is carried out, and the achievements and existing shortcomings in related fields are deeply excavated. …”
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A Representation-Learning-Based Graph and Generative Network for Hyperspectral Small Target Detection by Yunsong Li, Jiaping Zhong, Weiying Xie, Paolo Gamba

“…The mini-batch-training pattern of the GCN decreases the high computational cost of building an adjacency matrix for high-dimensional data sets. …”
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Application of Deep Learning on Global Spaceborne Radar and Multispectral Imagery for the Estimation of Urban Surface Height Distribution by Vivaldi Rinaldi, Masoud Ghandehari

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Multi-criteria computational screening of [BMIM][DCA]@MOF composites for CO2 capture by Mengjia Sheng, Xiang Zhang, Hongye Cheng, Zhen Song, Zhiwen Qi

“…In this work, hypothetical IL@MOFs were computationally constructed and screened by integrating molecular simulation and convolutional neural network (CNN) for CO2 capture. First, the IL [BMIM][DCA] with a large CO2 solubility was inserted into 1631 pre-selected Computational-Ready Experimental (CoRE) MOFs to create hypothetical IL@MOFs. …”
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Efficient Segmentation Using Attention-Fusion Modules With Dense Predictions by Serdar Erisen

“…Fusing the multi-scale global and local semantic information remains a challenging task for foundation models with computational costs and the need for effective long-range recognition. …”
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Intelligent Operation and Maintenance of Wind Turbines Gearboxes via Digital Twin and Multi-Source Data Fusion by Tiantian Xu, Xuedong Zhang, Wenlei Sun, Binkai Wang

“…Furthermore, an algorithm model for multi-source operational data analysis of wind turbines is designed, leveraging a Whale Optimization Algorithm-optimized Temporal Convolutional Network with an Attention mechanism (WOA-TCN-Attention). …”
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Predicting microbe-disease associations via graph neural network and contrastive learning by Cong Jiang, Cong Jiang, Junxuan Feng, Junxuan Feng, Bingshen Shan, Bingshen Shan, Qiyue Chen, Jian Yang, Jian Yang, Gang Wang, Gang Wang, Xiaogang Peng, Xiaozheng Li, Xiaozheng Li

“…Then, we design a feature encoder that combines graph convolutional network and graph attention mechanism to learn the node features of networks, and propose a feature dual-fusion module to effectively integrate node features from each layer's output. …”
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Characterizing Storm-Induced Coastal Flooding Using SAR Imagery and Deep Learning by Deanna Edwing, Lingsheng Meng, Suna Lv, Xiao-Hai Yan

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A Fusion Deep Learning Model for Predicting Adverse Drug Reactions Based on Multiple Drug Characteristics by Qing Ou, Xikun Jiang, Zhetong Guo, Jiayi Jiang, Zhanpeng Gan, Fangfang Han, Yongming Cai

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Multi-scale window transformer for cervical cytopathology image recognition by Jiaxiang Yi, Xiuli Liu, Shenghua Cheng, Li Chen, Shaoqun Zeng

“…Our design enables long-range feature integration but avoids whole image self-attention (SA) in ViT or twice local window SA in Swin Transformer. We find convolutional feed-forward networks (CFFN) are more efficient than original MLP-based FFN for representing cytopathology images. …”
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Enhancing basal cell carcinoma classification in preoperative biopsies via transfer learning with weakly supervised graph transformers by Johan Björkman, Sigrid Lagerroth, Jan Siarov, Filmon Yacob, Noora Neittaanmäki

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Spatial Prediction of Soil Continuous and Categorical Properties Using Deep Learning Approaches for Tamil Nadu, India by Thamizh Vendan Tarun Kshatriya, Ramalingam Kumaraperumal, Sellaperumal Pazhanivelan, Nivas Raj Moorthi, Dhanaraju Muthumanickam, Kaliaperumal Ragunath, Jagadeeswaran Ramasamy

“…In this study, soil continuous (pH and OC) and categorical variables (order and suborder) were predicted using deep learning–multi layer perceptron (DL-MLP) and one-dimensional convolutional neural networks (1D-CNN) for the entire state of Tamil Nadu, India. …”
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METHOD FOR EVALUATING EXCAVATION SHORING DESIGN CONCEPTS by Yu. G. Zheglova, B. P. Titarenko

“…The study is based on dynamic programming methods based on the principle of Bellman optimality. Logical convolution matrices are applied according to the dichotomy method.Results. …”
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Selective Reading for Arabic Sentiment Analysis by Mohamed Zouidine, Mohammed Khalil

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