(IoT) Network intrusion detection system using optimization algorithms by Luo Shan

“…Compared with traditional models like the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) and Support Vector Machine (SVM), the proposed framework significantly improves the sensitivity and generalization ability for detecting various types of attacks through dynamic feature selection and parameter optimization. Experimental results demonstrate that the hybrid algorithm exhibits superior real-time responsiveness in binary classification tasks, thanks to its lightweight design that reduces dependency on computational resources. …”
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Fault Diagnosis Method of Rolling Bearing Based on 1D Multi-Channel Improved Convolutional Neural Network in Noisy Environment by Huijuan Guo, Dongzhi Ping, Lijun Wang, Weijie Zhang, Junfeng Wu, Xiao Ma, Qiang Xu, Zhongyu Lu

“…By introducing BiLSTM, an attention mechanism and a local sparse structure of a two-channel Convolutional Neural Network, the feature information of the noisy timing signal is fully extracted at different scales while reducing the computational parameters. …”
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Semantic Segmentation Method for High-Resolution Tomato Seedling Point Clouds Based on Sparse Convolution by Shizhao Li, Zhichao Yan, Boxiang Ma, Shaoru Guo, Hongxia Song

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Dataset of SCAPS-1D simulated halide perovskite solar cells with SHAP and machine learning-based PCE optimizationZenodo by Ivan E. Novoselov, Alexander M. Gvozdev, Andrey A. Smirnov, Ivan S. Zhidkov

“…Additional files include detailed material databases featuring physical properties of electron and hole transport layers—such as band gap energies, electron affinities, dielectric permittivity, and carrier mobilities—organized in separate spreadsheets.Moreover, the repository offers a pre-trained CatBoost machine learning to predict the solar cell efficiency, along with a Jupyter Notebook that outlines the data preprocessing, machine learning workflow, and feature importance analysis via SHAP values. …”
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A Lightweight Model for Shine Muscat Grape Detection in Complex Environments Based on the YOLOv8 Architecture by Changlei Tian, Zhanchong Liu, Haosen Chen, Fanglong Dong, Xiaoxiang Liu, Cong Lin

“…Automated harvesting of “Sunshine Rose” grapes requires accurate detection and classification of grape clusters under challenging orchard conditions, such as occlusion and variable lighting, while ensuring that the model can be deployed on resource- and computation-constrained edge devices. This study addresses these challenges by proposing a lightweight YOLOv8-based model, incorporating DualConv and the novel C2f-GND module to enhance feature extraction and reduce computational complexity. …”
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Using lightweight method to detect landslide from satellite imagery by Jinchi Dai, Xiaoai Dai, Renyuan Zhang, JiaXin Ma, Wenyu Li, Heng Lu, Weile Li, Shuneng Liang, Tangrui Dai, Yunfeng Shan, Donghui Zhang, Lei Zhao

“…Increasingly general-purpose detection models are being deployed for these complex and dynamic tasks involving features that are difficult to characterize. However, these models are computationally expensive and memory-hungry, while the accuracy and detection efficiency remain wanting. …”
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Digital Twin-Supported Battery State Estimation Based on TCN-LSTM Neural Networks and Transfer Learning by Kai Zhao, Ying Liu, Yue Zhou, Wenlong Ming, Jianzhong Wu

“…In this study, we propose a DT-supported battery state estimation method, in collaboration with the temporal convolutional network (TCN) and the long short-term memory (LSTM), to address the challenge of feature extraction. Firstly, we introduce a 4-layer hierarchical DT to overcome computational and data storage limitations in conventional battery management systems. …”
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Mamba-YOLO-ML: A State-Space Model-Based Approach for Mulberry Leaf Disease Detection by Chang Yuan, Shicheng Li, Ke Wang, Qinghua Liu, Wentao Li, Weiguo Zhao, Guangyou Guo, Lai Wei

“…Traditional detection methods relying on chemical pesticides and manual observation prove inefficient and unsustainable. Although computer vision and deep learning technologies offer new solutions, existing models exhibit limitations in natural environments, including low recognition rates for small targets, insufficient computational efficiency, poor adaptability to occlusions, and inability to accurately identify structural features such as leaf veins. …”
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TLDDM: An Enhanced Tea Leaf Pest and Disease Detection Model Based on YOLOv8 by Jun Song, Youcheng Zhang, Shuo Lin, Huijie Han, Xinjian Yu

“…Initially, the C2f-Faster-EMA module is employed to reduce the number of parameters and model complexity while enhancing image feature extraction capabilities. …”
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PCPE-YOLO with a lightweight dynamically reconfigurable backbone for small object detection by Weijia Chen, Jiaming Liu, Tong Liu, Yaoming Zhuang

“…Finally, we integrate an Efficient Up-Convolution Block to sharpen decoder feature maps, enhancing small object recall with minimal computational overhead. …”
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Novel channel attention-based filter pruning methods for low-complexity semantic segmentation models by Md. Bipul Hossain, Na Gong, Mohamed Shaban

“…This is realized by recognizing the contextual importance of the feature maps in each layer of the models and the significance of each filter to the final model performance. …”
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Bioinformatic Analysis of WNT Family Proteins by Konstantin Midlovets, Natalia Volkova, Mykyta Peka

“…In this study, in silico methods were used to analyze the structural features of WNT proteins. Specifically, the isoelectric points, GRAVY scores, aliphatic indices, and instability indices were determined, and correlation analysis was performed to examine relationships between the latter three parameters. …”
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Research on Personnel Image Segmentation Based on MobileNetV2 H-Swish CBAM PSPNet in Search and Rescue Scenarios by Di Zhao, Weiwei Zhang, Yuxing Wang

“…By substituting ResNet50 with the more efficient MobileNetV2 as the model backbone, the computational complexity is significantly reduced. Furthermore, replacing the ReLU6 activation function in MobileNetV2 with H-Swish enhances segmentation accuracy without increasing the parameter count. …”
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Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning by Johan Helleberg, Anna Sundelin, Johan Mårtensson, Olav Rooyackers, Ragnar Thobaben

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The entropy-transformed Gompertz distribution: Distributional insights and cross-disciplinary utilizations

“…Some of its core characteristics, such as its statistical and computational features, are clearly presented. A thorough simulation analysis has been done to examine the final behavior of maximum likelihood estimators while estimating model parameters. …”
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LSENet: A Lightweight Spectral Enhancement Network for High-Quality Speech Processing on Resource-Constrained Platforms by Hyeong Il Koh, Sungdae Na, Myoung Nam Kim

“…Additionally, to capture the long-range contextual dependencies of the extracted features, an improved dual-path recurrent neural network is introduced between the encoder and decoder structures. …”
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YOLO-LPSS: A Lightweight and Precise Detection Model for Small Sea Ships by Liran Shen, Tianchun Gao, Qingbo Yin

“…The characteristics of YOLO-LPSS are as follows: (1) Strengthening the backbone’s ability to extract and emphasize features relevant to small ship objects, particularly in semantic-rich layers. (2) A sophisticated, learnable method for up-sampling processes is employed, taking into account both deep image information and semantic information. (3) Introducing a post-processing mechanism in the final output of the resampling process to restore the missing local region features in the high-resolution feature map and capture the global-dependence features. …”
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RDCU-Net: A Multi-Scale Residual Dilated Convolution U-Net with Spatial Pyramid Pooling for Brain Tumor Segmentation by Mohammad Soltani-Gol, Akbar Asgharzadeh-Bonab, Hamid Soltanian-Zadeh, Jalil Mazlum

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String processing model for knowledge-driven systems by V. P. Ivashenko

“…The paper gives definitions of concepts necessary for the calculation of metric features calculated over strings. As a result of the experiments, theoretical estimates of the computational complexity of the implemented operations and the validity of the choice of parameters of the used data structures were confirmed, which ensures near-optimal throughput and operation time indicators of operations. …”
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Succulent Plant Image Classification Based on Lightweight GoogLeNet with CBAM Attention Mechanism by Xingyu Tong, Zhihong Liang, Fangrong Liu

“…The model parameters and computational complexity are significantly reduced by streamlining the Inception modules from nine to seven and introducing depth-separable convolution. …”
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