An MCMC Approach to Bayesian Image Analysis in Fourier Space by Konstantinos Bakas, John Kornak, Hernando Ombao

“…Bayesian image analysis methods are commonly applied to solve image analysis problems such as noise reduction, feature enhancement, and object detection. A primary limitation of these methods is their computational cost due to the complex joint interdependencies between pixels, which limits the efficiency of performing posterior sampling through Markov chain Monte Carlo (MCMC). …”
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A lightweight UAV target detection algorithm based on improved YOLOv8s model by Fubao Ma, Ran Zhang, Bowen Zhu, Xirui Yang

“…Extensive experiments on the VisDrone2019 dataset show that the proposed model reduces parameters by 37.9 $$\%$$ , computational cost by 22.8 $$\%$$ , and model size by 36.9 $$\%$$ , while improving AP, AP50, and AP75 by 0.2 $$\%$$ , 0.2 $$\%$$ , and 0.4 $$\%$$ , respectively. …”
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Efficient Dynamic Performance Prediction of Railway Bridges Situated on Small-Radius Reverse Curves by Yumin Song, Bin Hu, Xiaoliang Meng

“…After identifying essential design parameters as data features using Fisher scores, we proceed to input these features into a support vector machine (SVM). …”
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LiteTom-RTDETR: A Lightweight Real-Time Tomato Detection System for Plant Factories by Wenshuai Liu, Qingzheng Liu, Wenyong Quan, Junli Wang, Xiaomin Yao, Qiang Liu, Yuxiang Tian

“…This model employed RepViT as a lightweight backbone network instead of the original RTDETR backbone, considerably reducing both the number of parameters in and computational complexity of the model. …”
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Binary and Multi-Class Classification of Colorectal Polyps Using CRP-ViT: A Comparative Study Between CNNs and QNNs by Jothiraj Selvaraj, Fadhiyah Almutairi, Shabnam M. Aslam, Snekhalatha Umapathy

“…In addition to the key metrics, computational parameters were compared, where CRP_QNN-ViT excelled in computational time. …”
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A Lightweight Citrus Ripeness Detection Algorithm Based on Visual Saliency Priors and Improved RT-DETR by Yutong Huang, Xianyao Wang, Xinyao Liu, Liping Cai, Xuefei Feng, Xiaoyan Chen

“…Experiments on our dataset show that LightSal-RTDETR achieves a mAP@50 of 81%, improving by 1.9% over the original model while reducing parameters by 28.1% and computational cost by 26.5%. …”
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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. …”
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Identification of digital twins to guide interpretable AI for diagnosis and prognosis in heart failure by Feng Gu, Andrew J. Meyer, Filip Ježek, Shuangdi Zhang, Tonimarie Catalan, Alexandria Miller, Noah A. Schenk, Victoria E. Sturgess, Domingo Uceda, Rui Li, Emily Wittrup, Xinwei Hua, Brian E. Carlson, Yi-Da Tang, Farhan Raza, Kayvan Najarian, Scott L. Hummel, Daniel A. Beard

“…Unsupervised machine learning applied to digital twin-derived features identified interpretable phenogroups and mechanistic drivers of cardiovascular death risk. …”
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FPGA-oriented lightweight multi-modal free-space detection network by Feiyi Fang, Junzhu Mao, Wei Yu, Jianfeng Lu

“…The pruning is in two parts. For the feature extractors, we propose a data-dependent filter pruner according to the principle that the low-rank feature map contains less information. …”
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A lightweight model for echo trace detection in echograms based on improved YOLOv8 by Jungang Ma, Jianfeng Tong, Minghua Xue, Junfan Yao

“…It reduces computational complexity by 18.5%, decreases model parameters by 40%, and improves mAP0.5 to 79.5% and mAP0.5:0.95 to 58.2%, making it suitable for echosounders with limited resources.…”
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Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM by Han Peiqing

“…A model experiment containing internal and external personality tendency classification, anxiety, and depression dichotomy was designed using logistic regression analysis, information entropy, and SVM algorithm to construct the feature dimensions of the network behavior data, combined with the labeled data of mental state to derive the sample data set for model experiments. …”
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Deep Separable Hypercomplex Networks by Nazmul Shahadat, Anthony S. Maida

“…Deep hypercomplex-inspired convolutional neural networks (CNNs) have recently enhanced feature extraction for image classification by allowing weight sharing across input channels. …”
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ZZ-YOLOv11: A Lightweight Vehicle Detection Model Based on Improved YOLOv11 by Zhe Zhang, Zhongyang Zhang, Gang Li, Chenxi Xia

“…Secondly, to reduce the number of parameters in the detection head and to fuse the extracted features better, a self-developed Lightweight Detail Convolutional Detection Head (LDCD) detection head is introduced. …”
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Design and Research on a Reed Field Obstacle Detection and Safety Warning System Based on Improved YOLOv8n by Yuanyuan Zhang, Zhongqiu Mu, Kunpeng Tian, Bing Zhang, Jicheng Huang

“…The improved model reduces parameter count and computational complexity by 31.9% and 33.4%, respectively, with a model size of only 4.2 MB. …”
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SGSNet: a lightweight deep learning model for strawberry growth stage detection by Zhiyu Li, Jianping Wang, Guohong Gao, Yufeng Lei, Chenping Zhao, Yan Wang, Haofan Bai, Yuqing Liu, Xiaojuan Guo, Qian Li

“…An innovative lightweight convolutional neural network, named GrowthNet, is designed as the backbone of SGSNet, facilitating efficient feature extraction while significantly reducing model parameters and computational complexity. …”
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A Lightweight and High-Performance YOLOv5-Based Model for Tea Shoot Detection in Field Conditions by Zhi Zhang, Yongzong Lu, Yun Peng, Mengying Yang, Yongguang Hu

“…Deep learning is well-suited for performing complex tasks due to its robust feature extraction capabilities. However, low-complexity models often suffer from poor detection performance, while high-complexity models are hindered by large size and high computational cost, making them unsuitable for deployment on resource-limited mobile devices. …”
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FP-YOLOv8: Surface Defect Detection Algorithm for Brake Pipe Ends Based on Improved YOLOv8n by Ke Rao, Fengxia Zhao, Tianyu Shi

“…It reduces the model’s parameter count through its unique design. It achieves improved feature representation by adopting specific technique within its structure. …”
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YED-Net: Yoga Exercise Dynamics Monitoring with YOLOv11-ECA-Enhanced Detection and DeepSORT Tracking by Youyu Zhou, Shu Dong, Hao Sheng, Wei Ke

“…Furthermore, a Parallel Spatial Attention (PSA) mechanism is incorporated to enhance multi-target feature discrimination. These enhancements enable the model to achieve a high detection accuracy of 98.6% mAP@0.5 while maintaining low computational complexity (2.35 M parameters, 3.11 GFLOPs). …”
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Research on RF Intensity Temperature Sensing based on 1D-CNN by DING Meiqi, GUI Lin, WANG Ziyi, SHANG Disen, QIAN Min, LI Qiankun

“…【Conclusion】1D-CNN has significant advantages in dealing with complex nonlinear relationships and feature extraction, not only superior in computational efficiency and robustness, but also effective in dealing with noise and environmental interference. …”
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Optimized exposer region-based modified adaptive histogram equalization method for contrast enhancement in CXR imaging by Shivam Gangwar, Reeta Devi, Nor Ashidi Mat Isa

“…The proposed PSO-ERBMAHE method delivers high-quality contrast enhancement in medical imaging, ensuring better visibility of critical anatomical features. By strengthening fine details, maintaining mean brightness, and improving computational efficiency, this technique enhances disease examination and diagnosis, reducing misinterpretation risks and improving clinical decision-making.…”
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