Temporal and Modality Awareness-Based Lightweight Residual Network With Attention Mechanism for Human Activity Recognition Using a Lower-Limb Exoskeleton Robot by Chang-Sik Son, Won-Seok Kang

“…The model adopts an asymmetric convolutional architecture composed of depthwise and pointwise layers to efficiently capture temporal and modality-specific features while significantly reducing the number of trainable parameters. …”
Get full text

Design of an Iterative Method for Malware Detection Using Autoencoders and Hybrid Machine Learning Models by Rijvan Beg, R. K. Pateriya, Deepak Singh Tomar

Get full text

Question-matching approach based on gradual machine learning by Xuejian HE, Anqi CHEN, Zhiqiang GUO, Zhiru WANG, Qun CHEN

Get full text

Online Attentive Kernel-Based Off-Policy Temporal Difference Learning by Shangdong Yang, Shuaiqiang Zhang, Xingguo Chen

Get full text

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. …”
Get full text

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. …”
Get full text

Epileptic Seizure Detection in EEG Signals Using Machine Learning and Deep Learning Techniques by Hepseeba Kode, Khaled Elleithy, Laiali Almazaydeh

Get full text

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

Get full text

Voice as a sensitive biomarker for predicting exercise intensity: a modelling study by Shuyi Zhou, Ruisi Ma, Wangjing Hu, Dandan Zhang, Rui Hu, Shengwei Zou, Dingyi Cai, Zikang Jiang, Hexiao Ding, Ting Liu

“…Speech data were collected before, during, and after these activities using professional recording equipment. Acoustic features were extracted using the openSMILE toolkit, focusing on the Geneva Minimalistic Acoustic Parameter Set (GeMAPS) and the Computational Paralinguistics Challenge (ComParE) feature sets. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

YOLO-BCD: A Lightweight Multi-Module Fusion Network for Real-Time Sheep Pose Estimation by Chaojie Sun, Junguo Hu, Qingyue Wang, Chao Zhu, Lei Chen, Chunmei Shi

“…Comparative evaluations demonstrate significant improvements over baseline models, achieving 91.7% recognition accuracy with 389 FPS processing speed while maintaining 19.2% parameter reduction and 32.1% lower computational load compared to standard YOLOv8. …”
Get full text

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

Get full text

Modeling of a Novel T-Core Sensor with an Air Gap for Applications in Eddy Current Nondestructive Evaluation by Siquan Zhang

“…Compared to the finite element method (FEM), analytical approaches offer advantages, such as faster computation and high accuracy, enabling a comprehensive analysis of how sensor and material parameters influence defect detection outcomes. …”
Get full text

MRD: A Linear-Complexity Encoder for Real-Time Vehicle Detection by Kaijie Li, Xiaoci Huang

“…To address these limitations, this study introduces Mamba RT-DETR (MRD), an optimized architecture featuring three principal innovations: (1) We devise an efficient vehicle detection Mamba (EVDMamba) network that strategically integrates a linear-complexity state space model (SSM) to substantially mitigate computational overhead while preserving feature extraction efficacy. (2) To counteract the constrained receptive fields and suboptimal spatial localization associated with conventional SSM sequence modeling, we implement a multi-branch collaborative learning framework that synergistically optimizes channel dimension processing, thereby augmenting the model’s capacity to capture critical spatial dependencies. (3) Comprehensive evaluations on the BDD100K benchmark demonstrate that MRD architecture achieves a 3.1% enhancement in mean average precision (mAP) relative to state-of-the-art RT-DETR variants, while concurrently reducing parameter count by 55.7%—a dual optimization of accuracy and efficiency.…”
Get full text

Piecewise Linear and Nonlinear Window Functions for Modelling of Nanostructured Memristor Device by T.D. Dongale, P.J. Patil, K.P. Patil, S.B. Mullani, K.V. More, S.D. Delekar, P.K. Gaikwad, R.K. Kamat

Get full text

Multi-scale friction model for automotive brake system incorporating tribological effects of surface asperities by Jung Yun Won, Hyungjo Seo, Youngjae Kim, Hoejeong Jeong, Joonseok Kyeong, Woojeong Oh, Myoung-Gyu Lee

“…The model primarily focuses on overall material behavior and micromechanical deformation within the brake system while maintaining computational efficiency, providing a mechanics-based, multi-scale representation suitable for large-scale simulations in consideration of pressure, temperature, and topological parameters. …”
Get full text

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

Get full text

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. …”
Get full text