Autoencoder Reconstruction of Cosmological Microlensing Magnification Maps by Somayeh Khakpash, Federica B. Bianco, Georgios Vernardos, Gregory Dobler, Charles Keeton

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A Lightweight Barcode Detection Algorithm Based on Deep Learning by Jingchao Chen, Ning Dai, Xudong Hu, Yanhong Yuan

“…In the model’s neck, linear mapping and grouped convolution are used to improve the C2f module, and the ADown convolution block is utilized to modify the model’s downsampling, which reduces the model’s parameters and computational cost while improving the efficiency of model feature fusion. …”
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CDUNeXt: efficient ossification segmentation with large kernel and dual cross gate attention by Hailiang Xia, Chuantao Wang, Zhuoyuan Li, Yuchen Zhang, Shihe Hu, Jiliang Zhai

“…By designing lightweight module structures, utilizing large-kernel convolutions to extracts the long-distance dependencies of different features of the image, and adopting dual-cross-gate-attention(DCGA) to sequentially capture the channel and spatial dependencies so as to fast and accurate segmentation while maintaining fewer parameters and lower complexity. …”
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A Lightweight Forward–Backward Independent Temporal-Aware Causal Network for Speech Emotion Recognition by Sijia Fei, Qiang Feng, Fei Gao

“…Furthermore, the backward temporal-aware module uses dilated causal convolutions to learn backward information and weighted fusion of multi-level temporal features to enhance the perception of backward emotion changes. …”
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FruitsMultiNet: A deep neural network approach to identify fruits through multi-scale feature fusion using mobile interface by Tasauf Mim, Md Mahbubur Rahman, Jahanur Biswas, Ahmad Shafkat, Khandaker Mohammad Mohi Uddin

“…A reliable fruit classification system during harvest and post-harvest phases can minimize time, cost, and human error while modernizing the processes of sorting, labeling, and packaging. …”
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Landslide susceptibility assessment using lightweight dense residual network with emphasis on deep spatial features by Shenghua Xu, Zhuolu Wang, Jiping Liu, Xinrui Ma, Tingting Zhou, Qing Tang

“…To minimize computational costs, we design a depthwise separable residual module that optimizes traditional convolution on residual branches into depthwise separable convolution. …”
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Estimation of Fractal Dimensions and Classification of Plant Disease with Complex Backgrounds by Muhammad Hamza Tariq, Haseeb Sultan, Rehan Akram, Seung Gu Kim, Jung Soo Kim, Muhammad Usman, Hafiz Ali Hamza Gondal, Juwon Seo, Yong Ho Lee, Kang Ryoung Park

“…Therefore, previous studies have proposed disease classification methods based on machine learning or deep learning techniques; however, most did not consider real-world plant images with complex backgrounds and incurred high computational costs. To address these issues, this study proposes a computationally effective residual convolutional attention network (RCA-Net) for the disease classification of plants in field images with complex backgrounds. …”
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A Hybrid Deep Learning Framework for Deepfake Detection Using Temporal and Spatial Features by Fazeel Zafar, Talha Ahmed Khan, Salas Akbar, Muhammad Talha Ubaid, Sameena Javaid, Kushsairy Abdul Kadir

“…The system dissects video inputs into frames to extract features comprehensively by using Multi Test Convolutional Networks (MTCNN). This method ensures face detection and alignment by focusing on facial regions. …”
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Artificial Intelligence based Multi-sensor COVID-19 Screening Framework by Rakesh Chandra-Joshi, Malay Kishore-Dutta, Carlos M. Travieso

“… Many countries are struggling for COVID-19 screening resources which arises the need for automatic and low-cost diagnosis systems which can help to diagnose and a large number of tests can be conducted rapidly. …”
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LMSFA-YOLO: A lightweight target detection network in Remote sensing images based on Multiscale feature fusion by Yuanbo Chu, Jiahao Wang, Longhui Ma, Chenxing Wu

“…These methods optimize convolutional computation cost and enhance multiscale information extraction, significantly reducing computational cost and parameters, while improving feature representation and fusion without sacrificing accuracy. …”
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Improving vertical detail in simulated temperature and humidity data using machine learning by Joana D. da Silva Rodrigues, Cyril J. Morcrette

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Compressed CNN Plant Leaf Ｒecognition Model Fused with Bayesian by YAN Ming, ZHU Liang-kuan, JING Wei-peng

“…Aiming at the problem that there are many parameters in the process of plant leaf recognition and it is easy to produce over-fitting，in order to reduce the cost of storage and calculation，this paper proposes a plant leaf recognition convolutional neural network model based on Bayesian fusion. …”
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An automated platform to detect, assess, and quantify deterioration in concrete structures by Ibrahim Odeh, Behrouz Shafei

“…To move toward reducing inspection time, cost, and human error, the current study developed a deep convolutional neural network model tailored for detecting and quantifying deterioration in concrete structures. …”
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Colorectal Cancer Detection Tool Developed with Neural Networks by Alex Ede Danku, Eva Henrietta Dulf, Alexandru George Berciu, Noemi Lorenzovici, Teodora Mocan

“…Convolutional neural networks (CNNs) and artificial neural networks (ANNs) are utilized in conjunction with a graphical user interface (GUI), which caters to individuals lacking programming expertise. …”
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Generative Adversarial Network-Based Lightweight High-Dynamic-Range Image Reconstruction Model by Gustavo de Souza Ferreti, Thuanne Paixão, Ana Beatriz Alvarez

“…The proposed model is based on Generative Adversarial Networks and replaces traditional convolutions with depthwise separable convolutions, reducing the number of parameters while maintaining high visual quality and minimizing luminance artifacts. …”
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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

“…A novel architecture with a modified convolutional long short-term memory (ModConvLSTM) with pose heatmaps as input features is proposed to achieve human action recognition without a fixed frame number. …”
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Deep CNN ResNet-18 based model with attention and transfer learning for Alzheimer's disease detection by Sofia Biju Francis, Sofia Biju Francis, Jai Prakash Verma

“…Alzheimer's disease leads to a rapid and irreversible decline in cognitive abilities by damaging memory cells.MethodsA ResNet-18-based system is proposed, integrating Depth Convolution with a Squeeze and Excitation (SE) block to minimize tuning parameters. …”
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Research on Unsupervised Domain Adaptive Bearing Fault Diagnosis Method Based on Migration Learning Using MSACNN-IJMMD-DANN by Xiaoxu Li, Jiahao Wang, Jianqiang Wang, Jixuan Wang, Qinghua Li, Xuelian Yu, Jiaming Chen

“…To address the problems of feature extraction, cost of obtaining labeled samples, and large differences in domain distribution in bearing fault diagnosis on variable operating conditions, an unsupervised domain-adaptive bearing fault diagnosis method based on migration learning using MSACNN-IJMMD-DANN (multi-scale and attention-based convolutional neural network, MSACNN, improved joint maximum mean discrepancy, IJMMD, domain adversarial neural network, DANN) is proposed. …”
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Lightweight Pepper Disease Detection Based on Improved YOLOv8n by Yuzhu Wu, Junjie Huang, Siji Wang, Yujian Bao, Yizhe Wang, Jia Song, Wenwu Liu

“…First, the deformable convolutional module DCNv2 (Deformable ConvNetsv2) and the inverted residual mobile block iRMB (Inverted Residual Mobile Block) are introduced into the C2F module to improve the accuracy of the sampling range and reduce the computational amount. …”
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A Lightweight Network for Water Body Segmentation in Agricultural Remote Sensing Using Learnable Kalman Filters and Attention Mechanisms by Dingyi Liao, Jun Sun, Zhiyong Deng, Yudong Zhao, Jiani Zhang, Dinghua Ou

“…This paper proposed a lightweight and efficient learnable Kalman filter and Deformable Convolutional Attention Network (LKF-DCANet). The encoder is built using a shallow Channel Attention-Enhanced Deformable Convolution module (CADCN), while the decoder combines a Convolutional Additive Token Mixer (CATM) and a learnable Kalman filter (LKF) to achieve adaptive noise suppression and enhance global context modeling. …”
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