Skin cancer identification utilizing deep learning: A survey by Dulani Meedeniya, Senuri De Silva, Lahiru Gamage, Uditha Isuranga

“…Compared to existing survey studies, the authors address the latest related studies covering several public skin cancer image datasets and focusing on segmentation, classification based on convolutional neural networks and vision transformers, and explainability. …”
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Blockchain-Based Smart Monitoring Framework for Defense Industry by Abdullah Alqahtani, Shtwai Alsubai, Abed Alanazi, Munish Bhatia

“…The proposed method demonstrated the ability to accurately analyze an individual’s anomalous occurrences in activities using a hybrid Convolution Neural Network with Gated Recurrent Units. …”
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A Generation Algorithm for “Text to Image” Based on Multi-Channel Attention by Yang Yang, Ainuddin Wahid Bin Abdul Wahab, Norisma Binti Idris, Dingguo Yu, Chang Liu

“…Additionally, a feature fusion enhancement module is introduced, which combines low-resolution features from the previous stage with high-resolution features from the current stage. This allows the generation network to fully utilize the rich semantic information of low-level features and the high-resolution details of high-level features, ultimately producing high-quality, realistic images. …”
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Non-Destructive Detection Method of Apple Watercore: Optimization Using Optical Property Parameter Inversion and MobileNetV3 by Zihan Chen, Haoyun Wang, Jufei Wang, Huanliang Xu, Ni Mei, Sixu Zhang

“…This map was then used to train the MobileNetV3 network with dilated convolution, resulting in a pre-trained model. …”
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Bird Species Detection Net: Bird Species Detection Based on the Extraction of Local Details and Global Information Using a Dual-Feature Mixer by Chaoyang Li, Zhipeng He, Kai Lu, Chaoyang Fang

“…The dual-branch feature mixer extracts features from dichotomous feature segments using global attention and deep convolution, expanding the network’s receptive field and achieving a strong inductive bias, allowing the network to distinguish between similar local details. …”
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Improving High-Precision BDS-3 Satellite Orbit Prediction Using a Self-Attention-Enhanced Deep Learning Model by Shengda Xie, Jianwen Li, Jiawei Cai

“…This study introduces a novel data-driven methodology, Sample Convolution and Interaction Network with Self-Attention (SCINet-SA), to augment dynamic methods and improve BDS-3 ultra-rapid orbit prediction. …”
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Dynamic atrous attention and dual branch context fusion for cross scale Building segmentation in high resolution remote sensing imagery by Yaohui Liu, Shuzhe Zhang, Xinkai Wang, Rui Zhai, Hu Jiang, Lingjia Kong

“…Therefore, this study proposed a new semantic segmentation network named SegTDformer to extract buildings in remote sensing images. …”
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YOLO-RDM: Innovative Detection Methods for Eggplants and Stems in Complex Natural Environment by Qin Liu, Zhibin Zhou, Lu Xiong, Meilian Lu, Jingwen Ouyang

“…To achieve rapid identification of eggplants and stems in complex environments and enhance overall accuracy, this study establishes a database for eggplants and stems and proposes an efficient detection model based on the YOLOv8n network. First, a lightweight Receptive-Field Attention Convolution (RFAConv) and Mixed Local Channel Attention (MLCA) attention mechanism are used to design the C2f_RM module, replacing the C2f module in YOLOv8n to create a lightweight yet effective model. …”
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ST-MSRN: An enhanced spatio-temporal super-resolution model for complex meteorological data reconstruction by Ping Mei, Zhi Yang, Changzheng Liu, Lei Wang, Zixin Yin

“…To address these limitations, this study proposes a Spatio-Temporal Multi-Scale Residual Network (ST-MSRN), which integrates a Multi-Scale Residual Feature Block (MSRFB) with a Channel Stacking Mechanism. …”
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A Ship’s Maritime Critical Target Identification Method Based on Lightweight and Triple Attention Mechanisms by Pu Wang, Shenhua Yang, Guoquan Chen, Weijun Wang, Zeyang Huang, Yuanliang Jiang

“…This method is based on a triple attention mechanism designed to enhance the model’s ability to classify and recognize buoys of different colors in the channel while also making the feature extraction network more lightweight. First, the lightweight double convolution kernel feature extraction layer is constructed using group convolution technology to replace the Conv structure of YOLOv9 (You Only Look Once Version 9), effectively reducing the number of parameters in the original model. …”
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DVCW-YOLO for Printed Circuit Board Surface Defect Detection by Pei Shi, Yuyang Zhang, Yunqin Cao, Jiadong Sun, Deji Chen, Liang Kuang

“…First, all standard convolutions in the backbone and neck networks of YOLOv8n are replaced with lightweight DWConv convolutions. …”
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Artificial Intelligence-based Approaches for Characterizing Plaque Components From Intravascular Optical Coherence Tomography Imaging: Integration Into Clinical Decision Support Sy... by Michela Sperti, Camilla Cardaci, Francesco Bruno, Syed Taimoor Hussain Shah, Konstantinos Panagiotopoulos, Karim Kassem, Giuseppe De Nisco, Umberto Morbiducci, Raffaele Piccolo, Francesco Burzotta, Fabrizio D’Ascenzo, Marco Agostino Deriu, Claudio Chiastra

“…Deep learning models, particularly convolutional neural networks, further improve performance by enabling automatic feature extraction. …”
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Electroencephalogram-Based Emotion Classification Using Machine Learning and Deep Learning Techniques by Gst Ayu Vida Mastrika Giri, Made Leo Radhitya

“…A four-channel Muse EEG headband recorded neutral, negative, and positive emotions for the publicly available Feeling Emotions EEG dataset. Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were utilized for deep learning, while SVM, K-NN, and MLP were used for machine learning. …”
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Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology by Yinhu Gao, Peizhen Wen, Yuan Liu, Yahuang Sun, Hui Qian, Xin Zhang, Huan Peng, Yanli Gao, Cuiyu Li, Zhangyuan Gu, Huajin Zeng, Zhijun Hong, Weijun Wang, Ronglin Yan, Zunqi Hu, Hongbing Fu

“…In pathological analysis, using convolutional neural networks, multimodal pre-training models, etc., automatic tissue segmentation, tumor grading, and assisted diagnosis can be achieved, showing good scalability in interactive question-answering. …”
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Through-wall Human Pose Reconstruction and Action Recognition Using Four-dimensional Imaging Radar by Rui ZHANG, Hanqin GONG, Ruiyuan SONG, Yadong LI, Zhi LU, Dongheng ZHANG, Yang HU, Yan CHEN

“…This network overcomes the limitations of mainstream deep learning libraries that currently lack 4D convolution capabilities, which hinders the effective use of multiframe three-Dimensional (3D) voxel spatiotemporal domain information. …”
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PZS‐Net: Incorporating of Frame Sequence and Multi‐Scale Priors for Prostate Zonal Segmentation in Transrectal Ultrasound by Jianguo Ju, Qian Zhang, Pengfei Xu, Tiange Liu, Cheng Li, Ziyu Guan

“…To overcome the limitation, a novel Prostate Zonal Segmentation Network (PZS‐Net), based on U‐Net, which learns critical cross‐frame information and multi‐scale features from sequential frames, is proposed. …”
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Deep learning-based strategies for evaluating and enhancing university teaching quality by Ying Gao

“…This study aims to address these issues by leveraging deep learning techniques, specifically Convolutional Neural Networks (CNNs), to accurately assess and enhance the quality of university teaching. …”
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Research Advances in Underground Bamboo Shoot Detection Methods by Wen Li, Qiong Shao, Fan Guo, Fangyuan Bian, Huimin Yang

“…To address these, an integrated intelligent framework is proposed: (1) three-dimensional modeling via multi-sensor fusion for subsurface mapping; (2) artificial intelligence (AI)-driven harvesting robots with adaptive excavation arms and obstacle avoidance; (3) standardized cultivation systems to optimize soil conditions; (4) convolution neural network–transformer hybrid models for visual-aided radar image analysis; and (5) aeroponic AI systems for controlled growth monitoring. …”
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An RNN-CNN-Based Parallel Hybrid Approach for Battery State of Charge (SoC) Estimation Under Various Temperatures and Discharging Cycle Considering Noisy Conditions by Md. Shahriar Nazim, Md. Minhazur Rahman, Md. Ibne Joha, Yeong Min Jang

“…To address this issue, this work proposes a new hybrid method that integrates a gated recurrent unit (GRU), temporal convolution network (TCN), and attention mechanism. …”
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Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review by Ricardo Vardasca, Joaquim Gabriel Mendes, Carolina Magalhaes

“…Learning approaches based on support vector machines and artificial neural networks seem to be preferred, with a recent focus on convolutional neural networks. …”
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