Semantic segmentation of underwater images based on the improved SegFormer by Bowei Chen, Bowei Chen, Wei Zhao, Wei Zhao, Qiusheng Zhang, Mingliang Li, Mingyang Qi, You Tang, You Tang, You Tang

“…Compared to the standard SegFormer, it demonstrates improvements of 3.73% in MIoU, 1.98% in mRecall, 3.38% in mPrecision, and 2.44% in mF1score, with an increase of 9.89M parameters. The results demonstrate that the proposed method achieves superior segmentation accuracy with minimal additional computation, showcasing high performance in underwater image segmentation.…”
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A Lightweight Semantic Segmentation Model for Underwater Images Based on DeepLabv3+ by Chongjing Xiao, Zhiyu Zhou, Yanjun Hu

“…The framework employs MobileOne-S0 as the lightweight backbone for feature extraction, integrates Simple, Parameter-Free Attention Module (SimAM) into deep feature layers, replaces global average pooling in the Atrous Spatial Pyramid Pooling (ASPP) module with strip pooling, and adopts a content-guided attention (CGA)-based mixup fusion scheme to effectively combine high-level and low-level features while minimizing parameter redundancy. …”
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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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A Pipeline for Multivariate Time Series Forecasting of Gas Consumption in Pelletization Process by Thadeu Pezzin Melo, Jefferson Andrade, Karin Satie Komati

“…In step (iii), twelve features were identified as the most relevant based on the Random Forest importance index. …”
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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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False-Alarm-Controllable Detection of Marine Small Targets via Improved Concave Hull Classifier by Sainan Shi, Jiajun Wang, Jie Wang, Tao Li

“…On the other hand, in 3D feature space, an improved concave hull classifier is developed to further shrink the decision region, where a fast two-stage parameter search is designed for low computational cost and accurate control of false alarm rate. …”
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Deep learning to evaluate seismic-induced soil liquefaction and modified transfer learning between various data sources by Hongwei Guo, Chao Zhang, Hongyuan Fang, Timon Rabczuk, Xiaoying Zhuang

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A lightweight multi-path convolutional neural network architecture using optimal features selection for multiclass classification of brain tumor using magnetic resonance images by Amreen Batool, Yung-Cheol Byun

“…Pre-trained models like AlexNet, Residual Networks (ResNet), and Inception V3 are effective but has high computational costs due to trainable parameters. Therefore, a lightweight Multi -path Convolutional Neural Network (M-CNN) is introduced to extract features using varying convolutional filters at each convolutional layer. …”
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An improved EAE-DETR model for defect detection of server motherboard by Jian Chi, Mingke Zhang, Puhon Zhang, Guowang Niu, Zhihao Zheng

“…Furthermore, the model demonstrates a reduction in parameter count by 21.7% and a decrease in computational load by 12.0%. …”
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ELD-YOLO: A Lightweight Framework for Detecting Occluded Mandarin Fruits in Plant Research by Xianyao Wang, Yutong Huang, Siyu Wei, Weize Xu, Xiangsen Zhu, Jiong Mu, Xiaoyan Chen

“…Our method incorporates edge-aware processing to strengthen feature representation, introduces a streamlined detection head that balances accuracy with computational cost, and employs an adaptive upsampling strategy to minimize information loss during feature scaling. …”
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NeuroPong: the event-based camera driven embedded neuromorphic system by Charles P Rizzo, Bryson Gullett, Alex M Crumley, Maxwell E Marcum, Mason R Hyman, Carter Earheart-Brown, Julia Steed, Frank Standaert, Catherine D Schuman, James S Plank

“…Neuromorphic computing is a novel style of computing that features low-power spiking neural networks (SNNs) as the main compute components. …”
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A fuzzy-optimized multi-level random forest (FOMRF) model for the classification of the impact of technostress by Gabriel James, Ifeoma, David, John, Samuel, Enefiok, Imeh Umoren, Ubong Etuk, Aloysius, Anietie, Saviour, Chikodili

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Protocol for quantifying muscle fiber size, number, and central nucleation of mouse skeletal muscle cross-sections using Myotally software by Pieter Both, Soochi Kim, Jengmin Kang, Marina Arjona, Daniel I. Benjamin, Christopher W. Nutter, Armon Goshayeshi, Thomas A. Rando

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Lightweight Evolving U-Net for Next-Generation Biomedical Imaging by Furkat Safarov, Ugiloy Khojamuratova, Misirov Komoliddin, Ziyat Kurbanov, Abdibayeva Tamara, Ishonkulov Nizamjon, Shakhnoza Muksimova, Young Im Cho

“…This study aims to develop a lightweight and scalable U-Net-based architecture that enhances segmentation performance while substantially reducing computational overhead. <b>Methods</b>: We propose a novel evolving U-Net architecture that integrates multi-scale feature extraction, depthwise separable convolutions, residual connections, and attention mechanisms to improve segmentation robustness across diverse imaging conditions. …”
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A genetic programming approach with adaptive region detection to skin cancer image classification by Kunjie Yu, Jintao Lian, Ying Bi, Jing Liang, Bing Xue, Mengjie Zhang

“…In recent years, the development of computer vision and machine learning has provided new methods for assisted diagnosis. …”
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Lightweight Brain Tumor Segmentation Through Wavelet-Guided Iterative Axial Factorization Attention by Yueyang Zhong, Shuyi Wang, Yuqing Miao, Tao Zhang, Haoliang Li

“…Conventional deep learning methods, such as convolutional neural networks and transformer-based models, frequently introduce significant computational overhead or fail to effectively represent multi-scale features. …”
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Machine Learning Approach to Characterize Ferromagnetic La0.7Sr0.3MnO3 Thin Films via Featurization of Surface Morphology by Sanghyeok Ryou, Jihyun Lim, Minwoo Jang, Kitae Eom, Sunwoo Lee, Hyungwoo Lee

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Global miniaturization of broadband antennas by prescreening and machine learning by Slawomir Koziel, Anna Pietrenko-Dabrowska, Ubaid Ullah

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Enhancing sarcasm detection in sentiment analysis for cyberspace safety using advanced deep learning techniques by Raghu Dhumpati, Archana Sasi, Shaik Johny Basha, Suryanarayana Vadhri, P. Saroja, Swathi Alluri, Ramesh Vatambeti

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Variational Methods in Optical Quantum Machine Learning by Marco Simonetti, Damiano Perri, Osvaldo Gervasi

“…The computing world is rapidly evolving and advancing, with new ground-breaking technologies emerging. …”
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