A modified deep neural network enables identification of foliage under complex background by Xiaolong Zhu, Junhao Zuo, Honge Ren

“…Incorporating deep residual learning module into Inception V3 can not only save the computational cost by factorising convolutions, but also mitigate the vanishing gradients causing the increasing depth of the network. …”
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ANN-SVM-IP: An Innovative Method for Rapidly and Efficiently Detecting and Classifying of External Defects of Apple Fruits by Nashaat M. Hussain Hassan, Mohamed M. Hassan Mahmoud, Mohamed A. Ismeil, M. Mourad Mabrook, A. A. Donkol, A. M. Mabrouk

“…The proposed strategy combines accuracy, rapidity, and affordable implementation cost. The first phase attempts to detect exterior defects in apples by applying two proposed convolution kernels that were capable of identifying damaged sections of apples. …”
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Using intelligence techniques to automate Oracle testing by Nour Sulaiman, safwan hasson

“…This test leads to the consumption of time and cost and may lead to errors in which the tester falls. …”
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Detection of SAR Image Multiscale Ship Targets in Complex Inshore Scenes Based on Improved YOLOv5 by Zhixu Wang, Guangyu Hou, Zhihui Xin, Guisheng Liao, Penghui Huang, Yonghang Tai

“…Finally, to reduce the number of parameters and computational cost during model training, the normal convolution in the neck part is replaced with Ghost convolution. …”
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Intelligent Identification of Tea Plant Seedlings Under High-Temperature Conditions via YOLOv11-MEIP Model Based on Chlorophyll Fluorescence Imaging by Chun Wang, Zejun Wang, Lijiao Chen, Weihao Liu, Xinghua Wang, Zhiyong Cao, Jinyan Zhao, Man Zou, Hongxu Li, Wenxia Yuan, Baijuan Wang

“…Second, to achieve efficient feature upsampling, enhance the efficiency and accuracy of feature extraction, and reduce computational redundancy and memory access volume, the EUCB (Efficient Up Convolution Block), iRMB (Inverted Residual Mobile Block), and PConv (Partial Convolution) modules were introduced into the YOLOv11 model. …”
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Enhancing practicality and efficiency of deepfake detection by Ismael Balafrej, Mohamed Dahmane

“…Furthermore, some key considerations were identified to significantly reduce the size of the core convolutional neural network. The experiment yielded competitive results when evaluated on two second-generation deepfake datasets, namely Celeb-DFv2 and DFDC, while requiring only a fraction of the typical computational cost and resources.…”
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Fast QTMT partition decision based on deep learning by Shuang PENG, Xiaodong WANG, Zongju PENG, Fen CHEN

“…Compared with the predecessor standards, versatile video coding (VVC) significantly improves compression efficiency by a quadtree with nested multi-type tree (QTMT) structure but at the expense of extremely high coding complexity.To reduce the coding complexity of VVC, a fast QTMT partition method was proposed based on deep learning.Firstly, an attention-asymmetric convolutional neural network was proposed to predict the probability of partition modes.Then, the fast decision of partition modes based on the threshold was proposed.Finally, the cost of coding performance and time was proposed to obtain the optimal threshold, and the threshold decision method was proposed.Experimental results at different levels show that the proposed method achieves an average time saving of 48.62%/52.93%/62.01% with the negligible BDBR of 1.05%/1.33%/2.38%.Such results demonstrate that the proposed method significantly outperforms other state-of-the-art methods.…”
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Machine Learning-Based Quantification of Lateral Flow Assay Using Smartphone-Captured Images by Anne M. Davis, Asahi Tomitaka

“…Despite its advantages as convenient and low-cost testing, it suffers from poor quantification capacity where only yes/no or positive/negative diagnostics are achieved. …”
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Bidirectional Mamba with Dual-Branch Feature Extraction for Hyperspectral Image Classification by Ming Sun, Jie Zhang, Xiaoou He, Yihe Zhong

“…The HSI classification methods based on convolutional neural networks (CNNs) have greatly improved the classification performance. …”
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YOLO-SMUG: An Efficient and Lightweight Infrared Object Detection Model for Unmanned Aerial Vehicles by Xinzhe Luo, Xiaogang Zhu

“…The model incorporates an enhanced backbone architecture that integrates the lightweight Shuffle_Block algorithm and the Multi-Scale Dilated Attention (MSDA) mechanism, enabling effective small object feature extraction while significantly reducing parameter size and computational cost without compromising detection accuracy. Additionally, a lightweight inverted bottleneck structure, C2f_UIB, along with the GhostConv module, replaces the conventional C2f and standard convolutional layers. …”
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An Investigation on Prediction of Infrastructure Asset Defect with CNN and ViT Algorithms by Nam Lethanh, Tu Anh Trinh, Mir Tahmid Hossain

“…Convolutional Neural Networks (CNNs) have been demonstrated to be one of the most powerful methods for image recognition, being applied in many fields, including civil and structural health monitoring in infrastructure asset management. …”
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Clinical Applicability and Cross-Dataset Validation of Machine Learning Models for Binary Glaucoma Detection by David Remyes, Daniel Nasef, Sarah Remyes, Joseph Tawfellos, Michael Sher, Demarcus Nasef, Milan Toma

“…This study evaluates the clinical applicability and robustness of three machine learning models for automated glaucoma detection: a convolutional neural network, a deep neural network, and an automated ensemble approach. …”
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SYNTHESIS OF MOBILE OBJECTS COMPROMISABLE OPTIMAL TRAJECTORIES IN THE CONFLICT ENVIRONMENT by Albert M. Voronin, Yurii K. Ziatdinov, Oleksandr Y. Permiakov, Ihor D. Varlamov

“…The opposite optimization problem of active conflict subjects placement suggests that the mobile object motion to the target endpoint is maximum hampered, and the cost spending for system operation is minimized. …”
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LAVID: A Lightweight and Autonomous Smart Camera System for Urban Violence Detection and Geolocation by Mohammed Azzakhnini, Houda Saidi, Ahmed Azough, Hamid Tairi, Hassan Qjidaa

“…Centralized architectures do not present the ideal solution due to the high cost, processing time issues, and network bandwidth overhead. …”
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Methods of security situation prediction for industrial internet fused attention mechanism and BSRU by Xiangdong HU, Zhengguo TIAN

“…The security situation prediction plays an important role in balanced and reliable work for industrial internet.In the face of massive, high-dimensional and time-series data generated in the industrial production process, traditional prediction models are difficult to accurately and efficiently predict the network security situation.Therefore, the methods of security situation prediction for industrial internet fused attention mechanism and bi-directional simple recurrent unit (BSRU) were proposed to meet the real-time and accuracy requirements of industrial production.Each security element was analyzed and processed, so that it could reflect the current network state and facilitate the calculation of the situation value.One-dimensional convolutional network was used to extract the spatial dimension features between each security element and preserve the temporal correlation between features.The BSRU network was used to extract the time dimension features between the data information and reduced the loss of historical information.Meanwhile, with the powerful parallel capability of SRU network, the training time of model was reduced.Attention mechanism was introduced to optimize the correlation weight of BSRU hidden state to highlight strong correlation factors, reduced the influence of weak correlation factors, and realized the prediction of industrial internet security situation combining attention mechanism and BSRU.The comparative experimental results show that the model reduces the training time and training error by 13.1% and 28.5% than the model using bidirectional long short-term memory network and bidirectional gated recurrent unit.Compared with the convolutional and BSRU network fusion model without attention mechanism, the prediction error is reduced by 28.8% despite the training time increased by 2%.The prediction effect under different prediction time is better than other models.Compared with other prediction network models, this model achieves the optimization of time performance and uses the attention mechanism to improve the prediction accuracy of the model under the premise of increasing a small amount of time cost.The proposed model can well fit the trend of network security situation, meanwhile, it has some advantages in multistep prediction.…”
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Mastitis Classification in Dairy Cows Using Weakly Supervised Representation Learning by Soo-Hyun Cho, Mingyung Lee, Wang-Hee Lee, Seongwon Seo, Dae-Hyun Lee

“…Therefore, this study proposed a mastitis classification based on weakly supervised representation learning using an autoencoder on time series milking data, which allows for concurrent milking representation learning and weakly supervision with low-cost labels. The proposed method employed a structure where the classifier branches from the latent space of a 1D-convolutional autoencoder, enabling representation learning of milking data to be conducted from the perspective of reconstructing the original information and detecting mastitis. …”
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A lightweight remote sensing image detection model with feature aggregation diffusion network by Xiaohui Cheng, Xukun Wang, Yun Deng, Qiu Lu, Yanping Kang, Jian Tang, Yuanyuan Shi, Junyu Zhao

“…To address this, we propose LightFAD-YOLO, a lightweight model integrating feature aggregation diffusion for multi-scale context propagation, enhancing small object detection in complex scenes. The central convolutional detection head combines detail-enhanced convolution and group normalization, reducing computational costs by 23.4 % while maintaining precision. …”
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BGLE-YOLO: A Lightweight Model for Underwater Bio-Detection by Hua Zhao, Chao Xu, Jiaxing Chen, Zhexian Zhang, Xiang Wang

“…First, an efficient multi-scale convolutional EMC module is introduced to enhance the backbone network and capture the dynamic changes in targets in the underwater environment. …”
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3D Object Detection Based on Graph Network Fusion Sampling Strategy by LI Wenju, CHEN Zhilin, QU Jiantao, CUI Liu, CHU Wanghui, GAO Hui

“…In the 3D target detection technology based on point cloud, there are problems like high cost of point cloud calculation and large gap between target scales, which lead to low target detection efficiency. …”
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A Deep Learning and Transfer Learning Approach for Vehicle Damage Detection by Lin Li, Koshin Ono, Chun-Kit Ngan

“…Then a convolutional neural network (CNN) model is built to classify whether or not the vehicles have damages. …”
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