Lightweight Tea Shoot Picking Point Recognition Model Based on Improved DeepLabV3+ by HU Chengxi, TAN Lixin, WANG Wenyin, SONG Min

“…Identifying and locating the tender buds of famous and high-quality tea for picking is an important component of the modern tea picking robot. Traditional neural network methods suffer from issues such as large model size, long training times, and difficulties in dealing with complex scenes. …”
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Identification of spodumene using a remote-sensing index cube from SDGSAT-1 and other satellites by Siyuan Li, Nannan Zhang, Yong Li, Li Chen, Hao Zhang, Jinyu Chang, Jintao Tao, Jianpeng Jing

“…The model combines a convolute onal neural network (CNN) and a graph convolutional network (GCN), integrating spatial and spectral features to enhance identification accuracy. …”
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An Integrated Bearing Fault Diagnosis Method Based on Multibranch SKNet and Enhanced Inception-ResNet-v2 by Baoquan Hu, Jun Liu, Yue Xu, Tianlong Huo

“…Furthermore, the convolution structure in the Inception-ResNet-v2 network was replaced by the improved depthwise separable convolution network to achieve effective feature extraction. …”
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TCN-Based DDoS Detection and Mitigation in 5G Healthcare-IoT: A Frequency Monitoring and Dynamic Threshold Approach by Mirza Akhi, Ciaran Eising, Lubna Luxmi Dhirani

“…This research evaluates the proposed model against prior methods, including Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNNs), demonstrating improved accuracy. …”
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Explainable analysis of infrared and visible light image fusion based on deep learning by Bo Yuan, Hongyu Sun, YinJing Guo, Qiang Liu, Xinghao Zhan

“…Firstly, a multimodal image fusion model was proposed based on the advantages of convolutional neural networks (CNN) for local context extraction and Transformer global attention mechanism. …”
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Advancements and Challenges in Character Recognition: A Comparative Analysis of CNN and Deep Learning Approaches by Yang Ximin

“…This paper provides a comprehensive review of character recognition technologies, focusing on the application of Convolutional Neural Networks (CNN) and deep learning methodologies. …”
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Machine learning and facial recognition for down syndrome detection: A comprehensive review by Khosro Rezaee

“…This paper explores various facial analysis techniques, including deep convolutional neural networks (DCNNs) and hybrid models combining traditional image processing with deep learning. …”
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Advancements in Image Classification: From Machine Learning to Deep Learning by Cheng Haoran

“…These methods achieve image classification through two stages: feature extraction and classification, but they encounter limitations when confronted with large-scale datasets and complicated tasks. Convolutional Neural Networks (CNNs) have gradually replaced traditional methods in image classification due to the rise of deep learning, resulting in improved accuracy and robustness. …”
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Analysis of The Role of Deep Learning Models in Image Classification Applications by Li Xiang

“…The integration of deep learning models, particularly Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), has revolutionized this process, enabling the development of automated, fast, and practical systems. …”
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Multiscale Time-Frequency Sparse Transformer Based on Partly Interpretable Method for Bearing Fault Diagnosis by Shouquan Che, Jianfeng Lu, Congwang Bao, Caihong Zhang, Yongzhi Liu

“…Transformer model is being gradually studied and applied in bearing fault diagnosis tasks, which can overcome the feature extraction defects caused by long-term dependencies in convolution neural network (CNN) and recurrent neural network (RNN). …”
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Innovative approaches for skin disease identification in machine learning: A comprehensive study by Kuldeep Vayadande, Amol A. Bhosle, Rajendra G. Pawar, Deepali J. Joshi, Preeti A. Bailke, Om Lohade

“…Investigate the effectiveness and performance of several algorithms, such as the flexible k-nearest neighbor, the sturdy support vector machine (SVM), and the complex convolutional neural networks (CNNs), advanced techniques for automated skin disease detection encompass deep learning methods such as recurrent neural networks (RNNs) for sequential data processing, generative adversarial networks (GANs) for generating synthetic data, and attention mechanisms for focusing on relevant image regions by means of a thorough examination of the most recent studies. …”
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Augmented prediction of vertebral collapse after osteoporotic vertebral compression fractures through parameter-efficient fine-tuning of biomedical foundation models by Sibeen Kim, Inkyeong Kim, Woon Tak Yuh, Sangmin Han, Choonghyo Kim, Young San Ko, Wonwoo Cho, Sung Bae Park

“…To construct an accurate prediction model, we explored two backbone architectures: convolutional neural networks and vision transformers (ViTs), along with various pre-trained weights and fine-tuning methods. …”
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Efficient Intrusion Detection System Data Preprocessing Using Deep Sparse Autoencoder with Differential Evolution by Saranya N., Anandakumar Haldorai

“…The efficiency of the transformation methods is evaluated with recursive Pearson correlation-based feature selection and graphical convolution neural network (G-CNN) methods.…”
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Enhancing semantical text understanding with fine-tuned large language models: A case study on Quora Question Pair duplicate identification. by Sifei Han, Lingyun Shi, Fuchiang Rich Tsui

“…In our previous study, we developed a Siamese Convolutional Neural Network (S-CNN) that achieved an F1 score of 82.02% (95% C.I.: 81.83%-82.20%). …”
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Global surface eddy mixing ellipses: spatio-temporal variability and machine learning prediction by Tian Jing, Ru Chen, Chuanyu Liu, Chunhua Qiu, Chunhua Qiu, Cuicui Zhang, Mei Hong

“…We also assessed the predictability of global mixing ellipses using machine learning algorithms, including Spatial Transformer Networks (STN), Convolutional Neural Network (CNN) and Random Forest (RF), with mean-flow and eddy- properties as features. …”
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Implemantasi Mask R-CNN pada Perhitungan Tinggi dan Lebar Karang untuk Memantau Pertumbuhan Transplantasi Karang by Naufal Alkhalis, Husaini Husaini, Haekal Azief Haridhi, Cut Nadilla Maretna, Nur Fadli, Yudi Haditiar, Muhammad Nanda, Maria Ulfah, Kris Handoko, Intan Malayana, Arsa Cindy Safitri

“…Penelitian ini mengimplementasikan algoritma Mask Region-based Convolutional Neural Network (Mask R-CNN) dengan Pustaka Detectron2 dalam deteksi dan segmentasi objek untuk menghitung tinggi dan lebar karang transplantasi melalui citra. …”
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The application of deep learning in early enamel demineralization detection by Ketai He, Rongxiu Zhang, Muchun Liang, Keyue Tian, Kaihui Luo, Ruoshi Chen, Jianpeng Ren, Jiajun Wang, Juan Li

“…A total of 624 high-quality digital images captured under standardized conditions were used to construct a deep learning model based on the Mask region-based convolutional neural network (Mask R-CNN). The model was trained to automate the detection of enamel demineralization. …”
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Enhancing Drought Forecast Accuracy Through Informer Model Optimization by Jieru Wei, Wensheng Tang, Pakorn Ditthakit, Jiandong Shang, Hengliang Guo, Bei Zhao, Gang Wu, Yang Guo

“…This study employed the Informer model to forecast drought and conducted a comparative analysis with Autoregressive Integrated Moving Average (ARIMA), long short-term memory (LSTM), and Convolutional Neural Network (CNN) models. The findings indicate that the Informer model outperforms the other three models in terms of drought forecasting accuracy across all time scales. …”
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Determination of the melanin and anthocyanin content in barley grains by digital image analysis using machine learning methods by E. G. Komyshev, M. A. Genaev, I. D. Busov, M. V. Kozhekin, N. V. Artemenko, A.  Y. Glagoleva, V. S. Koval, D. A. Afonnikov

“…Four models based on computer vision techniques and convolutional neural networks of different architectures were developed to predict grain pigment composition from images. …”
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Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices by Caixia Hu, Jie Li, Yaxu Pang, Lan Luo, Fang Liu, Wenhao Wu, Yan Xu, Houyu Li, Bingcang Tan, Guilong Zhang

“…A machine learning (ML) model for predicting nitrate leaching was then developed, with the random forest (RF) model outperforming the support vector machine (SVM), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) models, achieving an R² of 0.75. …”
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