YOLOv8n-GBE: A Hybrid YOLOv8n Model With Ghost Convolutions and BiFPN-ECA Attention for Solar PV Defect Localization by Likitha Reddy Yeddula, Archana Pallakonda, Rayappa David Amar Raj, Rama Muni Reddy Yanamala, K. Krishna Prakasha, Mallempati Sunil Kumar

“…Reliable photovoltaic (PV) module defect detection is essential for maintaining long term energy efficiency and lowering solar power system maintenance costs. The deep learning model presented in this research is based on a hybridized YOLOv8n architecture and is lightweight and high performing. …”
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Determining optimal strategies for primary prevention of cardiovascular disease: a synopsis of an evidence synthesis study by Olalekan A Uthman, Lena Al-Khudairy, Chidozie Nduka, Rachel Court, Jodie Enderby, Seun Anjorin, Hema Mistry, G J Melendez-Torres, Sian Taylor-Phillips, Aileen Clarke

“…This series of systematic reviews and meta-analyses synthesised evidence on the effectiveness, comparative effectiveness and cost-effectiveness of pharmacological and non-pharmacological interventions for primary cardiovascular disease prevention. …”
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A High-Performance and Lightweight Maritime Target Detection Algorithm by Shidan Sun, Zhiping Xu, Xiaochun Cao, Jiachun Zheng, Jiawen Yang, Ni Jin

“…In the SFPF module, the ghost dynamic convolution combined with low-cost adaptive spatial feature fusion is proposed. …”
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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

“…Experiments show that CDUNeXt achieves the best segmentation performance with an optimal balance of lighter weights and less computational cost compared to existing methods. This work fills the gap in the application of deep learning techniques in the diagnosis of ligamentum flavum ossificans, contributes to the realization of lightweight medical image segmentation networks and lays the foundation for subsequent research.…”
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A Lightweight Forward–Backward Independent Temporal-Aware Causal Network for Speech Emotion Recognition by Sijia Fei, Qiang Feng, Fei Gao

“…Meanwhile, the numerical results show that the proposed method has a good application prospect with a small number of parameters (0.21M) and low computational cost (80.72 MFLOPs).…”
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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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Automated classification of oral potentially malignant disorders and oral squamous cell carcinoma using a convolutional neural network framework: a cross-sectional studyResearch in... by Cristina Saldivia-Siracusa, Eduardo Santos Carlos de Souza, Arnaldo Vitor Barros da Silva, Anna Luíza Damaceno Araújo, Caíque Mariano Pedroso, Tarcília Aparecida da Silva, Maria Sissa Pereira Sant'Ana, Felipe Paiva Fonseca, Hélder Antônio Rebelo Pontes, Marcos G. Quiles, Marcio Ajudarte Lopes, Pablo Agustin Vargas, Syed Ali Khurram, Alexander T. Pearson, Mark W. Lingen, Luiz Paulo Kowalski, Keith D. Hunter, André Carlos Ponce de Leon Ferreira de Carvalho, Alan Roger Santos-Silva

“…MobileNet displayed the lowest computational cost. Grad-CAM analysis demonstrated discrepancies between the best-performing model and the highest explainability model. …”
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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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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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Enhancing Indian summer monsoon prediction: Deep learning approach for skillful long-lead forecasts of rainfall by Kalpesh R. Patil, Takeshi Doi, J.V. Ratnam, Swadhin K. Behera

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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

“…Small data sets are also prone to local minima issues in the cost function. A scratch model that experiences extensive hyperparameter tuning may end up being either overfitted or underfitted. …”
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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

“…This method provides a technical basis for intensive cultivation and management of chili peppers, as well as efficiently and cost-effectively accomplishing the task of identifying chili pepper pests and diseases.…”
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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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Optimizing AlexNet for accurate tree species classification via multi-branch architecture and mixed-domain attention by Jianjianxian Liu, Tao Xing, Xiangyu Wang

“…The model integrates a multi-branch convolutional module, a mixeddomain attention module, and a joint loss function to improve feature extraction and class separation. …”
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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

“…The high accuracy and low false positive rates enable utilities to recover unbilled revenue and reduce inspection costs, amplifying savings compared to raw data or traditional approaches. …”
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Identification of Ground Fissures in Mining Areas from UAV Images Based on RDC-UNet by Zhu Huashan

“…Traditional geological exploration methods are inefficient and costly, making the precise detection of large-scale fissures difficult. …”
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