AERO: Adaptive Edge-Cloud Orchestration With a Sub-1K-Parameter Forecasting Model by Berend J. D. Gort, Godfrey M. Kibalya, Angelos Antonopoulos

“…With fewer than 1,000 parameters, AERO is highly suitable for deployment on edge devices with limited computational capacity. …”
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Research on Lightweight Small Object Detection Algorithm Based on Context Representation by Li Qiang, Cui Jianghui

“…In addition, the detection performance is superior to other traditional detection models under the condition of low parameter quantities and computational complexity. …”
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Lightweight Pyramid Cross-Attention Network for No-Service Rail Surface Defect Detection by Sixu Guo, Jiyou Fei, Liying Wang, Hua Li, Xiaodong Liu

“…However, many existing methods face challenges such as high parameters, complex computation, slow inspection speed, and low accuracy. …”
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Improved YOLOv8 Object Detection Method for Drone Aerial Images by Zhong Shuai, Wang Liping

“…The experimental results on the VisDrone2019 dataset show: compared with the YOLOv8 model, the BDI-YOLO model in accuracy mAP@50 and mAP@50:95 has increased by 3.8% and 2.7% respectively, with a 4% increase in recall, a 9.4% decrease in computational complexity, and a 28.8% decrease in parameter count. …”
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Underwater image enhancement via multiscale disentanglement strategy by Jiaquan Yan, Hao Hu, Yijian Wang, Muhammad Wasim Nawaz, Naveed Ur Rehman Junejo, Ente Guo, Huibin Feng

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Short-term PV power prediction based on meteorological similarity days and SSA-BiLSTM by Yikang Li, Wei Huang, Keying Lou, Xizheng Zhang, Qin Wan

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Facial morphology prediction after complete denture restoration based on principal component analysis by Cheng Cheng, Xiaosheng Cheng, Ning Dai, Tao Tang, Zhenteng Xu, Jia Cai

“…Firstly, the curvature feature template with few feature points is constructed to replace the deformed areas of facial models. …”
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Generating Human-Interpretable Rules from Convolutional Neural Networks by Russel Pears, Ashwini Kumar Sharma

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An Innovative Inversion Method of Potato Canopy Chlorophyll Content Based on the AFFS Algorithm and the CDE-EHO-GBM Model by Xiaofei Yang, Qiao Li, Honghui Li, Hao Zhou, Jinyan Zhang, Xueliang Fu

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ADMNet: adaptive deformable convolution large model combining multi-level progressive fusion for Building Change Detection by Liye Mei, Haonan Yu, Zhaoyi Ye, Chuan Xu, Cheng Lei, Wei Yang

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Small target detection in coal mine underground based on improved RTDETR algorithm by Feng Tian, Cong Song, Xiaopei Liu

“…This decreased the number of network parameters and computation. By introducing Deformable Attention in the coding part of the RTDETR algorithm, the deformable feature of this attention mechanism is used to improve the network’s ability to extract effective image features. …”
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An exploratory analysis of longitudinal artificial intelligence for cognitive fatigue detection using neurophysiological based biosignal data by Sameer Nooh, Mahmoud Ragab, Rania Aboalela, Abdullah AL-Malaise AL-Ghamdi, Omar A. Abdulkader, Ghadah Alghamdi

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Highly efficient stacking ensemble learning model for automated keratoconus screening by Zahra J. Muhsin, Rami Qahwaji, Ibrahim Ghafir, Mo’ath AlShawabkeh, Muawyah Al Bdour, Saif Aldeen AlRyalat, Majid Al-Taee

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RFAG-YOLO: A Receptive Field Attention-Guided YOLO Network for Small-Object Detection in UAV Images by Chengmeng Wei, Wenhong Wang

“…To address these challenges, we propose the receptive field attention-guided YOLO (RFAG-YOLO) method, an advanced adaptation of YOLOv8 tailored for small-object detection in UAV imagery, with a focus on improving feature representation and detection robustness. To this end, we introduce a novel network building block, termed the receptive field network block (RFN block), which leverages dynamic kernel parameter adjustments to enhance the model’s ability to capture fine-grained local details. …”
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PCLC-Net: Parallel Connected Lateral Chain Networks for Infrared Small Target Detection by Jielei Xu, Xinheng Han, Jiacheng Wang, Xiaoxue Feng, Zhenxu Li, Feng Pan

“…Given the widespread influence of U-Net and FPN network architectures on infrared small target detection tasks on existing models, these structures frequently incorporate a significant number of downsampling operations, thereby rendering the preservation of small target information and contextual interaction both challenging and computation-consuming. To tackle these challenges, we introduce a parallel connected lateral chain network (PCLC-Net), an innovative architecture in the domain of infrared small target detection, that preserves large-scale feature maps while minimizing downsampling operations. …”
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Extracting road maps from high-resolution satellite imagery using refined DSE-LinkNet by Prativa Das, Satish Chand

“…The experiments are performed on a publicly available dataset, DeepGlobe Road Extraction Challenge 2018, to show its efficacy over the D-LinkNet, winner of DeepGlobe Challenge 2018, by achieving IoU of 0.69 with lesser number of parameters and better computational complexity.…”
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Two stage malware detection model in internet of vehicles (IoV) using deep learning-based explainable artificial intelligence with optimization algorithms by Manal Abdullah Alohali, Sultan Alahmari, Mohammed Aljebreen, Mashael M. Asiri, Achraf Ben Miled, Sami Saad Albouq, Othman Alrusaini, Ali Alqazzaz

“…It impacts a vast amount of computers. Researchers have proposed numerous malware detection solutions for the past few years. …”
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An advanced fire detection system for assisting visually challenged people using recurrent neural network and sea-horse optimizer algorithm by Fahd N. Al-Wesabi, Abeer A. K. Alharbi, Ishfaq Yaseen

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Smart crop disease monitoring system in IoT using optimization enabled deep residual network by Ashish Saini, Nasib Singh Gill, Preeti Gulia, Anoop Kumar Tiwari, Priti Maratha, Mohd Asif Shah

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HFC-YOLO11: A Lightweight Model for the Accurate Recognition of Tiny Remote Sensing Targets by Jinyin Bai, Wei Zhu, Zongzhe Nie, Xin Yang, Qinglin Xu, Dong Li

“…Experimental results on the AI-TOD and VisDrone2019 datasets demonstrate that the improved model achieves mAP50 improvements of 3.4% and 2.7%, respectively, compared to the baseline YOLO11s, while reducing its parameters by 27.4%. Ablation studies validate the balanced performance of the hierarchical feature compensation strategy in the preservation of resolution and computational efficiency. …”
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