Enhancing knee osteoarthritis diagnosis with DMS: a novel dense multi-scale convolutional neural network approach by Di Zhang, Yuting Dong, Yao Xu, Junhui Qian, Miaoyu Ye, Qiang Yuan, Jian Luo

“…This module utilizes dense connections in convolutions of varying sizes (1 × 1, 3 × 3, 5 × 5) and across layers, enhancing feature reuse and complexity recognition, thereby improving recognition capabilities. …”
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Energetically self-sufficient robot group study kit by M. A. Rovbo, A. A. Malyshev

“…The article considers the project of educational and research software and hardware kit designed for use in case-based educational project activities based on the theme of energetically autonomous robots. An important feature of the complex is its interdisciplinarity since robotics combines many fields of science and technology — mechanical design, electronics, programming, elements of artificial intelligence, energy science and others. …”
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A Method for On-Board Fire Detection Based on the Integration of Expert Systems and Neural Networks by F. Wang, R. Yue, R. Sun, F. Liu, X. Kong

“…By integrating expert system feature maps for fire confirmation, the scheme meets the high-frequency inspection and rapid warning needs for fires, supporting the integrated application of satellite and ground systems, and will significantly enhance the early warning detection efficiency of satellite fire detection.…”
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Employing CNN mobileNetV2 and ensemble models in classifying drones forest fire detection images by Dima Suleiman, Ruba Obiedat, Rizik Al-Sayyed, Shadi Saleh, Wolfram Hardt, Yazan Al-Zain

“… In recent years, the adoption of advanced machine learning techniques has revolutionized approaches to solving complex problems, such as identifying occurrences of forest fires. …”
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No-Code Edge Artificial Intelligence Frameworks Comparison Using a Multi-Sensor Predictive Maintenance Dataset by Juan M. Montes-Sánchez, Plácido Fernández-Cuevas, Francisco Luna-Perejón, Saturnino Vicente-Diaz, Ángel Jiménez-Fernández

“…Edge Computing (EC) is one of the proposed solutions to address the problems that the industry is facing when implementing Predictive Maintenance (PdM) implementations that can benefit from Edge Artificial Intelligence (Edge AI) systems. …”
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Automated system of passenger accounting, committed illegal actions in trains of JSC “FPK” formation, and provision of information to train crews by A. T. Sungatullina, I. B. Gumina, V. A. Dobychina

“…Article tackles the solution of the problem of wrongdoing prevention in the train of JSC “FPK”, related to the lack of opportunity to provide immediate information to train crew on traveling passengers previously committed illegal actions. …”
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Real‐Time Monitoring of Ionosphere VTEC Using Multi‐GNSS Carrier‐Phase Observations and B‐Splines by Eren Erdogan, Michael Schmidt, Andreas Goss, Barbara Görres, Florian Seitz

“…Due to their localizing feature, B‐splines form a highly sparse structure in the filter measurement model. …”
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Tomato detection in natural environment based on improved YOLOv8 network by Wancheng Dong, Yipeng Zhao, Jiaxing Pei, Zuolong Feng, Zhikai Ma, Leilei Wang, Simon Shemin Wang

“…The method replaces the backbone network of the original YOLOv8 with a more lightweight MobileNetV3 structure to reduce the number of parameters of the model; at the same time, it integrates the convolutional attention mechanism module (CBAM) in the feature extraction network, which enhances the network's capability of extracting features of tomato fruits. …”
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AI bot to detect fake COVID‐19 vaccine certificate by Muhammad Arif, Shermin Shamsudheen, F Ajesh, Guojun Wang, Jianer Chen

“…So, to avoid this huge problem, this paper focuses on detecting fake vaccine certificates using a bot powered by Artificial Intelligence and neurologically powered by Deep Learning in which the following are the stages: a) Data Collection, b) Preprocessing to remove noise from the data, and convert to grayscale and normalised, c) Error level analysis, d) Texture‐based feature extraction for extracting logo, symbol and for the signature we extract Crest‐Trough parameter, and e) Classification using DenseNet201 and thereby giving the results as fake/real certificate. …”
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A Classifier Model Using Fine-Tuned Convolutional Neural Network and Transfer Learning Approaches for Prostate Cancer Detection by Murat Sarıateş, Erdal Özbay

“…Additionally, a pyramid-type CNN architecture was designed to simultaneously evaluate both fine details and broader structures by combining low- and high-resolution information through feature maps extracted from different CNN layers. …”
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Lightweight Multiscale Spatio-Temporal Graph Convolutional Network for Skeleton-Based Action Recognition by Zhiyun Zheng, Qilong Yuan, Huaizhu Zhang, Yizhou Wang, Junfeng Wang

“…Thirdly, the Spatio-Temporal Location Attention (STLAtt) module is used to identify the most informative joints in the sequence of skeletal information at a specific frame, hence improving the model’s ability to extract features and recognize actions. Finally, multi-stream data fusion input structure is used to enhance the input data and expand the feature information. …”
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PDAA: An End-to-End Polygon Dynamic Adjustment Algorithm for Building Footprint Extraction by Longjie Luo, Jiangchen Cai, Bin Feng, Liufeng Tao

“…Values of 75.4% AP and 84.9% PolySim were achieved on the WHU dataset, effectively solving the problems of redundant vertices and contour smoothing, and providing high-precision building vector data support for scenarios such as smart cities and emergency response.…”
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Visual Place Recognition Method Based on Attention Mechanism by DAI Tian-hong, YANG Xiao-yun, SONG Jie-qi

“…Finally, we combine the mask with the convolution feature to construct the image feature descriptor fused with the attention mechanism so as to improve the robustness of the feature. …”
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High order Interaction and Wavelet Convolution Network for visible infrared person reidentification by Li Ma, Rui Kong, XinGuan Dai

“…Furthermore, our framework uses wavelet convolution to mine more diverse features and solve the problem of insufficient feature extraction. …”
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ZFD-Net: Zinc flower defect detection model of galvanized steel surface based on improved YOLOV5. by Yang Gao, Hanquan Zhang, Lifu Zhu, Feitong Xie, Dong Xiao

“…Firstly, the model combined the YOLOV5 model with our proposed cross stage partial transformer (CSTR) module in this paper to increase the model receptive field and improve the global feature extraction (FE) capability. Secondly, we use bi-directional feature pyramid network (Bi-FPN) weighted bidirectional feature pyramid network to fuse defect details of different levels and scales to improve them. …”
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FAULT DIAGNOSIS BASED ON SEMI-SUPERVISED LLTSA FOR DIMENSION REDUCTION by LI Lei, PANG Hai, ZHANG QianTu

“…Aiming at this problem,semi-supervised linear local tangent space alignment（ SSLLTSA） dimension reduction method is proposed in this paper. …”
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FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON CHANNEL AND SPATIAL RECONSTRUCTION NETWORKS by ZHOU Tao, YAO DeChen, YANG JianWei

“…Since the fault vibration data collected in real engineering may be accompanied by noise，traditional diagnostic models are difficult to identify fault categories，to address this problem，a rolling bearing fault diagnosis research method based on channel and spatial reconstruction and progressive convolutional neural networks （CSRP-CNN） was proposed.The model utilizes channel and spatial reconstruction convolution （CSConv） to reduce the redundant information of channels and space in fault features，and reduces the complexity and computation to improve the performance； using convolutional block attention module （CBAM），attention enhancement operation was carried out in the channel and spatial dimensions to make the model pay attention to important fault feature information； and progressive convolutional network structure was used in the shallow layer of the network，which will fuse the previous fault feature information fused with the current input to obtain richer feature information.The performance of CSRP-CNN was evaluated by two different datasets of Case Western Reserve University（CWRU）and machinery fault simulator magnum（MFS-MG）.After the noise and ablation tests，it is verified that CSRP-CNN has strong robustness and the effects of CSConv，CBAM and progressive convolutional neural network（PCNN） on the model noise immunity performance.…”
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Fault diagnosis of rolling bearing based on channel and spatial reconstruction networks by ZHOU Tao, YAO Dechen, YANG Jianwei

“…The model utilized channel and spatial reconstruction convolution （CSConv） to reduce the redundant information of channels and space in fault features, and reduced the complexity and computation to improve the performance; using the convolutional block attention module (CBAM), attention enhancement operation was carried out in the channel and spatial dimensions to make the model pay attention to the important fault feature information; and the progressive convolutional network structure was used in the shallow layer of the network, which would fuse the previous fault feature information with the current input to obtain the richer feature information. …”
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Based on PCA and SSA-LightGBM oil-immersed transformer fault diagnosis method. by Jizhong Wang, Jianfei Chi, Yeqiang Ding, Haiyan Yao, Qiang Guo

“…The feature matrix is then standardized to obtain joint features. …”
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Multiscale Hjorth Descriptor on Epileptic EEG Classification by Achmad Rizal, Sugondo Hadiyoso, Suci Aulia, Inung Wijayanto, null Triwiyanto, Ziani Said

“…This process produces a feature vector that is used in the classification stage. …”
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