Real-time detection of Chinese cabbage seedlings in the field based on YOLO11-CGB by Hang Shi, Hang Shi, Changxi Liu, Changxi Liu, Miao Wu, Miao Wu, Hui Zhang, Hui Zhang, Hang Song, Hang Song, Hao Sun, Hao Sun, Yufei Li, Yufei Li, Jun Hu, Jun Hu

“…However, the similarity between the characteristics of Chinese cabbage seedlings and some weeds is a great challenge for accurate detection.MethodsThis study introduces an enhanced detection method for Chinese cabbage seedlings, employing a modified version of YOLO11n, termed YOLO11-CGB. The YOLO11n framework has been augmented by integrating a Convolutional Attention Module (CBAM) into its backbone network. …”
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Intrusion Detection System to Advance Internet of Things Infrastructure-Based Deep Learning Algorithms by Hasan Alkahtani, Theyazn H. H. Aldhyani

“…An IoTID20 dataset attack was employed to develop the proposed system; it is a newly generated dataset from the IoT infrastructure. In this framework, three advanced deep learning algorithms were applied to classify the intrusion: a convolution neural network (CNN), a long short-term memory (LSTM), and a hybrid convolution neural network with the long short-term memory (CNN-LSTM) model. …”
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Deep Learning Approaches for Retinal Disease Diagnosis: Insights from Fundus and OCT Analysis by Kumar Sarvesh, Dixit Vipul, Gupta Madhuri

“…We investigate state-of-the-art architectures, including Vision Transformers (ViT), Convolutional Neural Networks (CNN), and hybrid frameworks on various datasets, including OCT2017, EyePACS, ACRIMA, and others. …”
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Deep Learning–Based Precision Cropping of Eye Regions in Strabismus Photographs: Algorithm Development and Validation Study for Workflow Optimization by Dawen Wu, Yanfei Li, Zeyi Yang, Teng Yin, Xiaohang Chen, Jingyu Liu, Wenyi Shang, Bin Xie, Guoyuan Yang, Haixian Zhang, Longqian Liu

“…The preprocessing algorithm, based on a rotating bounding box detection framework, was trained and evaluated using precision, recall, and mean average precision (mAP) at various intersections over union thresholds. …”
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Evaluating CNN Architectures for the Automated Detection and Grading of Modic Changes in MRI: A Comparative Study by Li‐peng Xing, Gang Liu, Hao‐chen Zhang, Lei Wang, Shan Zhu, Man Du La Hua Bao, Yan‐ni Wang, Chao Chen, Zhi Wang, Xin‐yu Liu, Shuai Zhang, Qiang Yang

“…This study developed and investigated the performance of convolutional neural network (CNN) in detecting and grading MCs based on their maximum vertical extent. …”
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Traffic environment perception algorithm based on multi-task feature fusion and orthogonal attention by Zhengfeng LI, Mingen ZHONG, Yihong ZHANG, Kang FAN, Zhiying DENG, Jiawei TAN

“…This framework aims to achieve three essential objectives: vehicle target detection, extraction of drivable road areas, and segmentation of lane lines. …”
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A Hybrid Deep Learning–Based Approach for Visual Field Test Forecasting by Ashkan Abbasi, PhD, Sowjanya Gowrisankaran, PhD, Wei-Chun Lin, MD, PhD, Xubo Song, PhD, Bhavna Josephine Antony, PhD, Gadi Wollstein, MD, Joel S. Schuman, MD, Hiroshi Ishikawa, MD

“…This study introduces a hybrid deep learning framework to enhance flexibility and accuracy in VF test forecasting. …”
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Advanced Hydro-Informatic Modeling Through Feedforward Neural Network, Federated Learning, and Explainable AI for Enhancing Flood Prediction by Shahariar Hossain Mahir, Md Tanjum An Tashrif, Md Ahsan Karim, Dipanjali Kundu, Anichur Rahman, Md. Amir Hamza, Fahmid Al Farid, Abu Saleh Musa Miah, Sarina Mansor

“…To address this, our research adopts the Federated Learning (FL) framework in an effort to train state-of-the-art deep learning models like Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), Feed-Forward Neural Network (FNN) and Temporal Fusion Transformer-Convolutional Neural Network (TFT -CNN) on a 78-year dataset of rainfall, river flow, and meteorological variables from Sylhet and its upstream regions in Meghalaya and Assam, India. …”
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CrossModalSync: joint temporal-spatial fusion for semantic scene segmentation in large-scale scenes by Shuyi Tan, Yi Zhang, Yan Li, Byeong-Seok Shin

“…To address these, this paper introduces a new framework that balances accuracy and computational efficiency by utilizing temporal alignment (TA), projection multi-scale convolution (PMC), and priority point retention (PPR). …”
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Artificial intelligence in acupuncture: bridging traditional knowledge and precision integrative medicine by Guo-Liang Hou, Bao-Qiang Dong, Ben-Xing Yu, Jian-Yu Dai, Xing-Xing Lin, Ze-Zhong Cheng

“…We analyze key methodological innovations and challenges, and recommend future directions including the construction of federated multimodal data platforms, development of explainable AI frameworks, and promotion of open science practices. …”
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Research on Data Repair of Pile-Type Adjustable Wind Turbine Foundation Monitoring Based on FST-ATTNet by WEI Huanwei, ZHAO Jizhang, ZHENG Xiao, TAN Fang, LIU Cong

“…In the spatial domain, the Temporal Convolutional Network (TCN) models long-range dependencies by expanding causal convolutions, thereby capturing local and global spatial relationships. …”
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Video Coding Based on Ladder Subband Recovery and ResGroup Module by Libo Wei, Aolin Zhang, Lei Liu, Jun Wang, Shuai Wang

“…To address this issue, this paper proposes an innovative LadderConv framework, which combines discrete wavelet transform (DWT) with spatial and channel attention mechanisms. …”
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The Role of Time in Facial Dynamics and Challenges in Automatic Emotion Recognition (2019–2024) by Williams Contreras-Higuera, Lucrezia Crescenzi-Lanna

“…While deep learning (DL) techniques like convolutional neural networks (CNNs) and long short-term memory (LSTM) networks offer significant advances, they face challenges such as gradient vanishing and overfitting, particularly in long and complex sequences. …”
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Neural network-based symmetric encryption algorithm with encrypted traffic protocol identification by Jiakai Hao, Ming Jin, Yuting Li, Yuxin Yang

“…Special emphasis is placed on the performance of algorithms within both symmetric and asymmetric frameworks. Experimental results demonstrate the effectiveness of our proposed scheme, highlighting its potential for enhancing power grid security.…”
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AI-Driven Integration of Deep Learning With Lung Imaging, Functional Analysis, and Blood Gas Metrics for Perioperative Hypoxemia Prediction by Kecheng Huang, Chujun Wu, Rongpeng Pi, Jieyu Fang

“…Traditional diagnostic methods such as radiological imaging and ABG analysis often lack integrated predictive accuracy. AI frameworks, particularly convolutional neural networks and hybrid models like TD-CNNLSTM-LungNet, demonstrate exceptional performance in detecting pulmonary inflammation and stratifying hypoxemia risk, achieving up to 96.57% accuracy in pneumonia subtype differentiation and an area under the curve of 0.96 for postoperative hypoxemia prediction. …”
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Ocular Disease Detection Using Fundus Images: A Hybrid Approach of Grad-CAM and Multiscale Retinex Preprocessing With VGG16 Deep Features and Fine KNN Classification by Shreemat Kumar Dash, Kante Satyanarayana, Santi Kumari Behera, Sudarson Jena, Ashoka Kumar Ratha, Prabira Kumar Sethy, Aziz Nanthaamornphong

“…Ethical issues and legal frameworks are also crucial for the safe and fair implementation of AI in medical services. …”
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Intelligent Testing Method for Multi-Point Vibration Acquisition of Pile Foundation Based on Machine Learning by Ke Wang, Weikai Zhao, Juntao Wu, Shuang Ma

“…The model’s performance was assessed using statistical error metrics, including the correlation coefficient R², mean absolute error (MAE), and variance accounted for (VAF), with comparative evaluations conducted across different model frameworks. Results show that both the convolutional neural network (CNN) and the long short-term memory neural network (LSTM) consistently achieved high accuracy in identifying the location of the first reflection point in the pile shaft, with R² values greater than 0.98, MAE below 0.41 (m), and VAF greater than 98%. …”
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Machine Learning for Human Activity Recognition: State-of-the-Art Techniques and Emerging Trends by Md Amran Hossen, Pg Emeroylariffion Abas

“…The review also discusses traditional machine learning approaches, including supervised and unsupervised learning, alongside cutting-edge advancements in deep learning, such as convolutional and recurrent neural networks, attention mechanisms, and reinforcement learning frameworks. …”
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Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation by Olamilekan Shobayo, Reza Saatchi

“…Future research directions emphasize optimising deep learning models for real-time applications, enhancing interpretability, and integrating deep learning with existing healthcare frameworks for improved patient outcomes.…”
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Brain Representation in Conscious and Unconscious Vision by Ning Mei, David Soto

“…The development of robust frameworks to understand how the human brain represents conscious and unconscious perceptual contents is paramount to make progress in the neuroscience of consciousness. …”
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