LCAMNet: a lightweight model for apple leaf disease classification in natural environments by Yuanyuan Jiao, Honghui Li, Xueliang Fu, Buyu Wang, Buyu Wang, Kaiwen Hu, Shuncheng Zhou, Daoqi Han

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An overview of the activation functions used in deep learning algorithms by Mete Çelik, Kemal Adem, Serhat Kılıçarslan

“…Also, in deep learning algorithms, activation functions have been developed by taking into account features such as performing the learning process in a healthy way, preventing excessive learning, increasing the accuracy performance, and reducing the computational cost. …”
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Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition by Sk Mahmudul Hassan, Kumar Sekhar Roy, Ruhul Amin Hazarika, Mehbub Alam, Mithun Mukherjee

“…The proposed IEViT architecture extracts local as well as global features, which improves feature learning. The use of multiple filters with different kernel sizes efficiently uses computing resources to extract relevant features without the need for deeper networks. …”
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Tailoring neuromuscular dynamics: A modeling framework for realistic sEMG simulation. by Alvaro Costa-Garcia, Shingo Shimoda, Akihiko Murai

“…This study introduces an advanced computational model for simulating surface electromyography (sEMG) signals during muscle contractions. …”
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Design of an Iterative Method for Malware Detection Using Autoencoders and Hybrid Machine Learning Models by Rijvan Beg, R. K. Pateriya, Deepak Singh Tomar

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Lightweight remote sensing ship detection algorithm based on YOLOv5s by Haochen WANG, Yuelan XIN, Jiang GUO, Qingqing WANG

“…MethodsFirst, the backbone network adopts the ShuffleNet v2 block stacking method, effectively reducing the number of network model parameters and improving the computational speed; second, a region selection module filter is designed to select regions of interest and extract effective features more fully; finally, a circular smooth label is introduced to calculate angle loss and perform rotation detection on remote sensing ship targets, while deformable convolution is used to adapt to geometric deformation and improve detection performance. …”
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A study on temperature monitoring method for inverter IGBT based on memory recurrent neural network by Yunhe Liu, Tengfei Guo, Jinda Li, Chunxing Pei, Jianqiang Liu

“…Existing temperature monitoring methods based on the electro-thermal coupling model have limitations, such as ignoring device interactions and high computational complexity. To address these issues, an analysis of the parameters influencing IGBT failure is conducted, and a temperature monitoring method based on the Macro-Micro Attention Long Short-Term Memory (MMALSTM) recursive neural network is proposed, which takes the forward voltage drop and collector current as features. …”
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Epileptic Seizure Detection in EEG Signals Using Machine Learning and Deep Learning Techniques by Hepseeba Kode, Khaled Elleithy, Laiali Almazaydeh

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Enhanced Conditional GAN for High-Quality Synthetic Tabular Data Generation in Mobile-Based Cardiovascular Healthcare by Malak Alqulaity, Po Yang

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Machine learning of 27Al NMR electric field gradient tensors for crystalline structures from DFT by He Sun, Shyam Dwaraknath, Handong Ling, Kristin A. Persson, Sophia E. Hayes

“…We developed a fast, low-cost machine learning model to predict EFG parameters based on local structural motifs and elemental parameters. …”
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Recognition of anxiety and depression using gait data recorded by the kinect sensor: a machine learning approach with data augmentation by Milad Shoryabi, Ahmad Hajipour, Afshin Shoeibi, Ali Foroutannia

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Machine Learning‐Accelerated Reconstruction of Periodic Nanostructures with X‐ray Fluorescence Spectroscopy Methods by Vinh‐Binh Truong, Analía Fernández Herrero, Kas Andrle, Victor Soltwisch, Philipp Hönicke

“…Abstract With advancements in the semiconductor industry, the complexity of three‐dimensional (3D) nanostructures becomes higher with continuously decreasing feature sizes. In order to monitor the processing steps, it is crucial to accurately determine the critical dimensions and composition of these nanostructures. …”
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Online Attentive Kernel-Based Off-Policy Temporal Difference Learning by Shangdong Yang, Shuaiqiang Zhang, Xingguo Chen

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Mapping cation-eutaxy ternary with a phenomenological model by Jongbum Won, Taeyoung Kim, Minwoo Lee, Daniel W. Davies, Giyeok Lee, Aron Walsh, Aloysius Soon, Jong-Young Kim, Wooyoung Shim

“…Abstract Predicting the stability of ternary compounds poses a significant challenge due to the complex interplay of atomic features. Existing approaches often struggle to integrate these parameters into a unified framework, particularly for cation-eutaxy ABX ternary systems, where subtle compositional and bonding interactions govern the dimensionality and stability of III‒V networks. …”
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Dielectric constant prediction in polymers: A chemical structure based approach by S. Sharifi, S. Bonardd, L.A. Miccio

“…We employed a curated dataset of nearly 1000 polymeric materials, from which we extracted unit cell parameters, atomic features, and tokenized atom-wise descriptors. …”
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IoT enabled health monitoring system using rider optimization algorithm and joint process estimation by J. Prabin Jose, G. Jaffino, Mohammed Al Awadh, Koppula Srinivas Rao, Yan Yafang, Krishna Moorthy Sivalingam

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A Real-Time Green and Lightweight Model for Detection of Liquefied Petroleum Gas Cylinder Surface Defects Based on YOLOv5 by Burhan Duman

“…The architecture integrates ghost convolution and ECA blocks to improve feature extraction with less computational overhead in the network’s backbone. …”
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UAV Target Segmentation Based on Depse Unet++ Modeling by Zhaoqi Hou, Yiqing Gu, Zhen Zheng, Yueqiang Li, Haojie Li

“…By introducing Squeeze-and-Excitation, the model’s ability to discriminate camouflaged targets in high-similarity backgrounds is improved; by incorporating a depth-separated convolutional design, the parameters and computational requirements for embedded device applications are significantly reduced; and employing Dropout technique to prevent overfitting with limited sample sizes, thus boosting the model’s adaptability and generalization across environments. …”
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Optimizing Boride Coating Thickness on Steel Surfaces Through Machine Learning: Development, Validation, and Experimental Insights by Selim Demirci, Durmuş Özkan Şahin, Sercan Demirci, Armağan Gümüş, Mehmet Masum Tünçay

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ET-Mamba: A Mamba Model for Encrypted Traffic Classification by Jian Xu, Liangbing Chen, Wenqian Xu, Longxuan Dai, Chenxi Wang, Lei Hu

“…During the fine-tuning phase, the agent attention mechanism is adopted in the feature extraction phase to achieve global information modeling at a low computational cost, and the SmoothLoss function is designed to solve the problem of the insufficient generalization ability of cross-entropy loss function during training. …”
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