Analysing and Forecasting the Energy Consumption of Healthcare Facilities in the Short and Medium Term. A Case Study by Ali Koç, Serap Ulusam Seçkiner

“…The approach adopted for predicting hospital energy consumption involves five steps: data acquisition, data pre-processing, data prediction, hyper-parameter optimisation and feature analysis. Furthermore, all regression algorithms have undergone hyper-parameter optimisation using random search, grid search and Bayesian optimisation to achieve the minimum prediction errors represented by different metrics. …”
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LBT-YOLO: A Lightweight Road Targeting Algorithm Based on Task Aligned Dynamic Detection Heads by Pei Tang, Zhenyu Ding, Minnan Jiang, Weikai Xu, Mao Lv

“…This detection head reduces the number of parameters by sharing the neck network features, and performs task decomposition alignment to achieve high accuracy target detection using dynamic convolution and dynamic feature selection. …”
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Enhancing lung disease diagnosis with deep-learning-based CT scan image segmentation by Rima Tri Wahyuningrum, Achmad Bauravindah, Indah Agustien Siradjuddin, Budi Dwi Satoto, Amillia Kartika Sari, Anggraini Dwi Sensusiati

“…Whereas on the Kaggle dataset it achieved a Dice coefficient of 0.961, IoU of 0.930, computational time of 1.189 s, and 9.16 million trainable parameters. …”
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YOLO-LSM: A Lightweight UAV Target Detection Algorithm Based on Shallow and Multiscale Information Learning by Chenxing Wu, Changlong Cai, Feng Xiao, Jiahao Wang, Yulin Guo, Longhui Ma

“…To address challenges such as large-scale variations, high density of small targets, and the large number of parameters in deep learning-based target detection models, which limit their deployment on UAV platforms with fixed performance and limited computational resources, a lightweight UAV target detection algorithm, YOLO-LSM, is proposed. …”
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ELNet: An Efficient and Lightweight Network for Small Object Detection in UAV Imagery by Hui Li, Jianbo Ma, Jianlin Zhang

“…Finally, to improve detection in UAV imagery with dense, small, and scale-varying objects, we propose DIMB-C3k2, an enhanced module built upon C3k2, which boosts feature extraction under complex conditions. Compared with YOLOv12n, ELNet achieves an 88.5% reduction in parameter count and a 52.3% decrease in FLOPs, while increasing mAP₅₀ by 1.2% on the VisDrone dataset and 0.8% on the HIT-UAV dataset, reaching 94.7% mAP₅₀ on HIT-UAV. …”
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Data Mining Techniques for Early Detection and Classification of Plant Diseases: An Optimization-Based Approach by Wagh Swapnil, Sharma Ruchi

“…Furthermore, low-level optimization techniques like genetic algorithms as well as particle swarm optimization are used to fine tune the specific model parameters and to reduce the computational overhead for improving the detection efficacy still more. …”
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MM-3D Unet: development of a lightweight breast cancer tumor segmentation network utilizing multi-task and depthwise separable convolution by Xian Wang, Wenzhi Zeng, Junzeng Xu, Senhao Zhang, Yuexing Gu, Benhui Li, Xueyang Wang

“…Background and objectivesThis paper introduces a novel lightweight MM-3DUNet (Multi-task Mobile 3D UNet) network designed for efficient and accurate segmentation of breast cancer tumors masses from MRI images, which leverages depth-wise separable convolutions, channel expansion units, and auxiliary classification tasks to enhance feature representation and computational efficiency.MethodsWe propose a 3D depth-wise separable convolution, and construct channel expansional convolution (CEC) unit and inverted residual block (IRB) to reduce the parameter count and computational load, making the network more suitable for use in resource-constrained environments. …”
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D-YOLO: A Lightweight Model for Strawberry Health Detection by Enhui Wu, Ruijun Ma, Daming Dong, Xiande Zhao

“…Key innovations include (1) replacing the original backbone with MobileNetv3 to optimize computational efficiency; (2) implementing a Bidirectional Feature Pyramid Network for enhanced multi-scale feature fusion; (3) integrating Contextual Transformer attention modules in the neck network to improve lesion localization; and (4) adopting weighted intersection over union loss to address class imbalance. …”
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YOLO11-ARAF: An Accurate and Lightweight Method for Apple Detection in Real-World Complex Orchard Environments by Yangtian Lin, Yujun Xia, Pengcheng Xia, Zhengyang Liu, Haodi Wang, Chengjin Qin, Liang Gong, Chengliang Liu

“…Furthermore, the distilled model significantly reduces parameters and doubles the inference speed (FPS), enabling rapid and precise apple detection in challenging orchard settings with limited computational resources.…”
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Innovative Lightweight Detection for Airborne Remote Sensing: Integrating G-Shuffle and Dynamic Multiscale Pyramid Networks by Ruofei Liang, Yigang Cen, Linna Zhang, Fugui Zhang, Yansen Huang, Fei Gan

“…Second, the G-Shuffle module is designed to significantly enhance feature extraction efficiency and interchannel information interaction, balancing computational complexity and detection accuracy. …”
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Fall detection method based on spatio-temporal coordinate attention for high-resolution networks by Xiaorui Zhang, Qijian Xie, Wei Sun, Ting Wang

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A dual-branch model combining convolution and vision transformer for crop disease classification. by Qingduan Meng, Jiadong Guo, Hui Zhang, Yaoqi Zhou, Xiaoling Zhang

“…A learnable parameter is used to achieve a linear weighted fusion of these two types of features. …”
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Dynamic convolutional model based on distribution-collaboration strategy for remote sensing scene classification by Chenjun Xu, Jingqian Shu, Zhenghan Wang, Jialin Wang

“…Secondly, an adaptive enhanced attention mechanism based on the Lie Group feature covariance matrix is designed to aggregate the essential attribute feature (EAF) of HRRSI, which can effectively deal with HRRSI without increasing the computational complexity and delay of the model with the increase of HRRSI resolution. …”
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Advancing Rice Disease Detection in Farmland with an Enhanced YOLOv11 Algorithm by Hongxin Teng, Yudi Wang, Wentao Li, Tao Chen, Qinghua Liu

“…It also lowers computational complexity and enhances local feature capture through the C3k2-CFCGLU block. …”
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Lightweight Multi-Head MambaOut with CosTaylorFormer for Hyperspectral Image Classification by Yi Liu, Yanjun Zhang, Jianhong Zhang

“…While transformers have been widely adopted for hyperspectral image classification due to their global feature extraction capabilities, their quadratic computational complexity limits their applicability for resource-constrained devices. …”
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TEBS: Temperature–Emissivity–Driven band selection for thermal infrared hyperspectral image classification with structured State-Space model and gated attention by Enyu Zhao, Nianxin Qu, Yulei Wang, Caixia Gao, Jian Zeng

“…Subsequently, a weight computation (WC) module, leveraging SSM and GAM, is developed to generate robust band weights by sequentially leveraging multi-scale LST features. …”
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Efficient automated detection of power quality disturbances using nonsubsampled contourlet transform & PCA-SVM by Pampa Sinha, Kaushik Paul, Asit Mohanty, IM Elzein, Chandra Sekhar Mishra, Mohamed Metwally Mahmoud, Daniel Eutyche Mbadjoun Wapet, Abdulrahman Al Ayidh, Ahmed Althobaiti, Hany S Hussein, Thamer AH Alghamdi, Ahmed M Ewais

“…These optimized features are used for training a multi-class support vector machine, with its parameters further optimized for enhanced classification accuracy. …”
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A Hybrid Mechanism to Detect DDoS Attacks in Software Defined Networks by ÙAfsaneh Banitalebi Dehkordi, MohammadReza Soltanaghaei, Farsad Zamani Boroujeni

“…DDoS (Distributed Denial-of-Service) attacks are among the cyberattacks that are increasing day by day and have caused problems for computer network servers. With the advent of SDN networks, they are not immune to these attacks, and due to the software-centric nature of these networks, this type of attack can be much more difficult for them, ignoring effective parameters such as port and Source IP in detecting attacks, providing costly solutions which are effective in increasing CPU load, and low accuracy in detecting attacks are of the problems of previously presented methods in detecting DDoS attacks. …”
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ILN-YOLOv8: A Lightweight Image Recognition Model for Crimped Wire Connectors by Xiaojian Zhou, Jicheng Kan, Nur Fatin Liyana Mohd Rosely, Xu Duan, Jiajing Cai, Zihan Zhou

“…Taking the original YOLOv8 model as a baseline, the new model enhances the ability to extract shallow features from small targets by increasing the P2 detection layer and improving the Feature Pyramid Network(FPN) and Path Aggregation Network(PAN) structures. …”
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Potato precision planter metering system based on improved YOLOv5n-ByteTrack by Cisen Xiao, Changlin Song, Junmin Li, Min Liao, Yongfan Pu, Kun Du

“…Initially, the C3-Faster module is introduced, which reduces the number of parameters and computational load while maintaining detection accuracy. …”
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