Nameplate Detection and Recognition of Smart Meter Communication Module Based on ResSE-SegNet by ZHAI Xiaohui, SUN Kai, ZHAO Jifu, SUN Yanling, XING Yu, GUO Kaixuan, WANG Haiying

“…The region where the manufacturer′s name is located in the image is segmented using a deep codec network structure, and an end-to-end convolutional neural network (CNN) model is constructed and trained to identify different manufacturers.Finally, the data set of the communication module image is obtained through the full-dimensional smart meter detection system, and the detection and recognition experiment of the communication module nameplate is carried out. …”
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Retrievals of Biomass Burning Aerosol and Liquid Cloud Properties from Polarimetric Observations Using Deep Learning Techniques by Michal Segal Rozenhaimer, Kirk Knobelspiesse, Daniel Miller, Dmitry Batenkov

“…We present a comparison between the different DL approaches, as well as their comparison to existing algorithms. …”
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Research on UAV Jamming Signal Generation Based on Intelligent Jamming by Haonan Xue, Zhihai Zhuo, Weihao Yan, Yuexia Zhang

“…This paper introduces an enhanced jamming signal generation algorithm built on the traditional convolutional autoencoder. Without relying on prior signal knowledge, the algorithm introduces complex convolutional networks and residual modules. …”
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The analysis of sculpture image classification in utilization of 3D reconstruction under K-means++ by Xuhui Wang

“…ResNet50 includes residual blocks, each containing multiple convolutional layers and a skip connection, enabling the network to learn differences between inputs and outputs rather than directly learning outputs, thus improving performance. …”
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AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction by Abdussalam Elhanashi, Sergio Saponara, Qinghe Zheng, Nawal Almutairi, Yashbir Singh, Shiba Kuanar, Farzana Ali, Orhan Unal, Shahriar Faghani

“…Moreover, the need for strong applicable models across different populations and imaging modalities are addressed. …”
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Cloud Computing Resource Scheduling Algorithm Based on Unsampled Collaborative Knowledge Graph Network by Haichuan Sun, Liang Gu, Chenni Dong, Xin Ma, Zeyu Liu, Zhenxi Li

“…The knowledge graph data fragments are processed based on class convolution and human-machine interaction attention mechanism, and different sizes of linear aggregators are used to capture deep level information, completing the design of cloud computing resource scheduling algorithm. …”
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PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning by Boris I. Evstatiev, Dimitar T. Trifonov, Katerina G. Gabrovska-Evstatieva, Nikolay P. Valov, Nicola P. Mihailov

“…They confirmed once again that the trained convolutional neural network can be successfully used to evaluate the soiling state of photovoltaic surfaces.…”
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Review on Hybrid Deep Learning Models for Enhancing Encryption Techniques Against Side Channel Attacks by Amjed A. Ahmed, Mohammad Kamrul Hasan, Azana H. Aman, Nurhizam Safie, Shayla Islam, Fatima A. Ahmed, Thowiba E. Ahmed, Bishwajeet Pandey, Leila Rzayeva

“…Deep learning is being used in many different fields in the past several years. Convolutional neural networks and recurrent neural networks, for instance, have demonstrated efficacy in text generation and object detection in images, respectively. …”
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Seismic Random Noise Attenuation Using DARE U-Net by Tara P. Banjade, Cong Zhou, Hui Chen, Hongxing Li, Juzhi Deng, Feng Zhou, Rajan Adhikari

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Real-Time Human Action Recognition With Dynamical Frame Processing via Modified ConvLSTM and BERT by Raden Hadapiningsyah Kusumoseniarto, Zhi-Yuan Lin, Shun-Feng Su, Pei-Jun Lee

“…The effects of ModConvLSTM are verified at different depths. In our proposed architecture, we replace global average pooling (GAP) with Bidirectional Encoder Representations from Transformers (BERT) to address the limitations of temporal processing in a two-dimensional convolutional neural network (2D-CNN). …”
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Application of artificial intelligence in insect pest identification - A review by Sourav Chakrabarty, Chandan Kumar Deb, Sudeep Marwaha, Md. Ashraful Haque, Deeba Kamil, Raju Bheemanahalli, Pathour Rajendra Shashank

“…Deep learning algorithms, such as convolutional neural networks (CNNs), are primarily used for AI-powered insect pest identification by categorizing insects based on their visual features through image-based classification methodology. …”
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ResWLI: a new method to retrieve water levels in coastal zones by integrating optical remote sensing and deep learning by Nan Xu, Huichao Xin, Jiarui Wu, Jiaqi Yao, He Ren, Han-Su Zhang, Hao Xu, Hong Luan, Dong Xu, Yongze Song

“…To address this issue, we developed a new water level inversion model using residual neural network (ResNet), named as ResWLI, which integrates remotely sensed satellite data with a convolutional neural network (CNN). The ResWLI model was comprehensively tested at 10 tide stations worldwide, each with varying tidal characteristics and ranges. …”
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PSDA: Pyramid Spatial Deformable Aggregation for Building Segmentation in Multiview Remote Sensing Images by Xuejun Huang, Yi Wan, Yongjun Zhang, Xinyi Liu, Bin Zhang, Yameng Wang, Haoyu Guo, Yingying Pei, Zhonghua Hu

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AI-Powered Dental Forensics in Transforming Age Estimation Techniques: A Narrative Review by Parul Khare, Kalyani Bhargava, M Siddharth, Deepak Bhargava, Anoushka Chauhan

“…It discusses the evolution from conventional methods to fast and efficient AI models, including Convolutional Neural Networks (CNN), Deep Learning (DL) and Machine Learning (ML) and compares the efficiency of each to conclude which AI model yields the best results for researchers. …”
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Comparative analysis of data transformation methods for detecting non-technical losses in electricity grids by Maria Gabriel Chuwa, Daniel Ngondya, Rukia Mwifunyi

“…While feature-based detection methods perform well, they often require manual feature engineering and domain expertise. Convolutional neural networks (CNN) have emerged as effective tools for automatically extracting features from raw data, but raw data often lacks the structure needed for optimal feature extraction. …”
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DSNET: A Lightweight Segmentation Model for Segmentation of Skin Cancer Lesion Regions by Yucong Chen, Guang Yang, Xiaohua Dong, Junying Zeng, Chuanbo Qin

“…To reduce the model size and guarantee model segmentation performance, we proposed a detail-enhanced separable difference convolution as a base module in the model. …”
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YOLO-EFM: Efficient traffic flow monitoring algorithm with enhanced multi-level information fusion by Shizhou Xu, Kaidi Cui

“…The study establishes a generalized efficient layer aggregation network incorporating Sobel convolution and develops a novel feature focus module that effectively aggregates information from different feature map levels. …”
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Vision-based detection algorithm for monitoring dynamic change of fire progression by Yongyoon Suh

“…This study aims to define vision-based patterns of fire events to identify multiple objects that contribute to different types of fire accidents. To achieve this, a convolutional neural network (CNN) based on deep learning is applied to detect fire events through vision-based patterns. …”
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A Closed-loop Detection Algorithm Based on Dynamic Time Warping by ZHU Tian, PENG Zhichuan, ZHANG Zhiteng, ZHU Zemin, XIE Yongbo, WANG Wenming

“…At the same time, it uses convolutional neural network to extract image features and the results are more advanced and abstract, and this enhances the robustness of the algorithm. …”
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Abnormal traffic detection method based on LSTM and improved residual neural network optimization by Wengang MA, Yadong ZHANG, Jin GUO

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