Gait Perception via Actual and Estimated Pneumatic Physical Reservoir Output by Junyi Shen, Tetsuro Miyazaki, Swaninda Ghosh, Toshihiro Kawase, Kenji Kawashima

“…Traditional approaches to gait perception—such as estimating joint angles and walking conditions—often rely on electronic sensors and neural networks, which can compromise wearability and impose high computational demands. …”
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Leveraging Extended Windows in End-to-End Deep Learning for Improved Continuous Myoelectric Locomotion Prediction by Yuzhou Lin, Yuyang Zhang, Wenjuan Zhong, Wenxuan Xiong, Zhen Xi, Yi-Feng Chen, Mingming Zhang

“…Current surface electromyography (sEMG) methods for locomotion mode prediction face limitations in anticipatory capability due to computation delays and constrained window lengths typically below 500 ms—a practice historically tied to stationarity requirements of handcrafted feature extraction. This study investigates whether end-to-end convolutional neural networks (CNNs) processing raw sEMG signals can overcome these constraints through extended window lengths (250 ms to 1500 ms). …”
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Deep Learning vs. Gradient Boosting: Optimizing Transport Energy Forecasts in Thailand Through LSTM and XGBoost by Thanapong Champahom, Chinnakrit Banyong, Thananya Janhuaton, Chamroeun Se, Fareeda Watcharamaisakul, Vatanavongs Ratanavaraha, Sajjakaj Jomnonkwao

“…This study addresses research gaps in transport energy forecasting by comparing Long Short-Term Memory (LSTM) neural networks and XGBoost models for predicting transport energy consumption in Thailand. …”
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MEViT: Generalization of Deepfake Detection With Meta-Learning EfficientNet Vision Transformer by Van-Nhan Tran, Hoanh-Su Le, Piljoo Choi, Suk-Hwan Lee, Ki-Ryong Kwon

“…Different facial forgery techniques result in complex data distributions, and most existing deepfake detection approaches rely on convolutional neural networks (CNNs) that treat the task as a binary classification problem. …”
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Early detection of Citrus Huanglongbing by UAV remote sensing based on MGA-UNet by Naibo Ye, Wenyong Mai, Feng Qin, Sen Yuan, Bo Liu, Zaiyuan Li, Conghui Liu, Fanghao Wan, Wanqiang Qian, Zhongzhen Wu, Xi Qiao

“…This study leverages multispectral imagery acquired via unmanned aerial vehicles (UAVs) and deep convolutional neural networks. This study introduce a novel model, MGA-UNet, specifically designed for HLB recognition. …”
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Enhancing Long-Term Flood Forecasting with SageFormer: A Cascaded Dimensionality Reduction Approach Based on Satellite-Derived Data by Fatemeh Ghobadi, Amir Saman Tayerani Charmchi, Doosun Kang

“…The integration of hydroclimate data with attention-based networks and dimensionality reduction demonstrates significant advancements over traditional approaches. …”
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FetalMovNet: A Novel Deep Learning Model Based on Attention Mechanism for Fetal Movement Classification in US by Musa Turkan, Emre Dandil, Furkan Erturk Urfali, Mehmet Korkmaz

“…The model integrates convolutional neural networks (CNN) for feature extraction and an attention mechanism to capture spatio-temporal patterns, significantly improving classification performance of fetal movements. …”
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Performance Analysis of Artificial Intelligence Models for Classification of Transmission Line Losses by Abraham O. Amole, Oluwagbemiga E. Ajiboye, Stephen Oladipo, Ignatius K. Okakwu, Ibrahim A. Giwa, Olamide O. Olusanya

“…Conventional approaches to analyzing power losses in electrical transmission networks have largely emphasized generic power loss minimization through the integration of loss-reducing devices such as shunt capacitors. …”
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Prompt-based fine-tuning with multilingual transformers for language-independent sentiment analysis by Faizad Ullah, Safiullah Faizullah, Imdad Ullah Khan, Turki Alghamdi, Toqeer Ali Syed, Ahmad B. Alkhodre, Muhammad Sohaib Ayub, Asim Karim

“…Baseline models are established using traditional machine learning approaches such as Support Vector Machines (SVM) and Logistic Regression, with feature extraction methods like TF-IDF. A hybrid deep learning model is introduced, combining Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs) to capture local and sequential text patterns. …”
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Exploring immune-inflammation markers in psoriasis prediction using advanced machine learning algorithms by Li Yang, Shixin He, Li Tang, Xiao Qin, Yan Zheng

“…Subsequently, nine classification algorithms were developed using the processed training set, including random forest, neural networks, XGBoost, k-nearest neighbors, gradient boosting, logistic regression, naïve Bayes, AdaBoost, and SVMs. …”
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Multisensor Remote Sensing and AI-Driven Analysis for Coastal and Urban Resilience Classification by Sumei Ren, Bushra Ghaffar, Muhammad Mubbin, Muhammad Haseeb, Zainab Tahir, Sher Shah Hassan, Dmitry E. Kucher, Olga D. Kucher, M. Abdullah-Al-Wadud

“…This article suggests a deep learning-based categorization framework for identifying resilience levels in urban and coastal settings by combining long short-term memory (LSTM) networks with multisensor remote sensing data. The Copernicus Marine Data Service's spatiotemporal ocean physics data, namely the eastward (uo) and northward (vo) seawater velocity, are used in the model to increase the precision of resilience evaluations. …”
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Examining the role of semiotics in social media-driven information campaigns by Mayor Inna Gurung, Nitin Agarwal

“…Findings show that posts featuring SCP symbols consistently received more interaction, even when follower counts were equal. …”
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Detecting shallow subsurface anomalies with airborne and spaceborne remote sensing: A review by Adam M. Morley, Tamsin A. Mather, David M. Pyle, J-Michael Kendall

“…The capabilities of each technique are evaluated by reviewing their ability to detect specific characteristics from subsurface anomalies and then they are tabulated by investigable feature and sensor type in seven technique tables. …”
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CareAssist GPT improves patient user experience with a patient centered approach to computer aided diagnosis by Ali Algarni

“…CareAssist-GPT combines high-resolution X-ray images, real-time physiological vital signs, and clinical notes within a unified predictive framework using deep learning. Feature extraction is performed using convolutional neural networks (CNNs), gated recurrent units (GRUs), and transformer-based NLP modules. …”
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Federated knee injury diagnosis using few shot learning by Chirag Goel, Anita X, Jani Anbarasi L

“…The proposed model used a 3DResNet50 architecture as the backbone to enhance both feature extraction and embedding representation. A combined Centralized and Federated Few-Shot Learning Framework is analysed to leverage episodic-intermittent training strategy based on Prototypical Networks. …”
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A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing (ELSA) by Charmayne Mary Lee Hughes, Yan Zhang, Ali Pourhossein, Terezia Jurasova

“…Early and accurate detection of frailty is essential for timely intervention, reducing its widespread impact on healthcare systems, social support networks, and economic stability.ObjectiveThis study aimed to classify frailty status into binary (frail vs. non-frail) and multi-class (frail vs. pre-frail vs. non-frail) categories. …”
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Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions by Mohamed Heshmat, Lyes Saad Saoud, Muayad Abujabal, Atif Sultan, Mahmoud Elmezain, Lakmal Seneviratne, Irfan Hussain

“…DL techniques enhance underwater SLAM by improving feature extraction, image denoising, distortion correction, and sensor fusion. …”
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A hybrid framework for colorectal cancer detection and U-Net segmentation using polynetDWTCADx by Akella S Narasimha Raju, K Venkatesh, Makineedi Rajababu, Ranjith Kumar Gatla, Marwa M. Eid, Enas Ali, Nataliia Titova, Ahmed B. Abou Sharaf

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Precision Enhanced Bioactivity Prediction of Tyrosine Kinase Inhibitors by Integrating Deep Learning and Molecular Fingerprints Towards Cost-Effective and Targeted Cancer Therapy by Fatma Hilal Yagin, Yasin Gormez, Cemil Colak, Abdulmohsen Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…This study presents a robust machine learning framework—leveraging deep artificial neural networks (dANNs), convolutional neural networks (CNNs), and structural molecular fingerprints—to accurately predict TKI bioactivity, ultimately accelerating the preclinical phase of drug development. …”
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YOLOv8-eRFD-AP: A Novel Domain Generalization Model for UAV-Based Insulator Inspection Under Adverse Weather Conditions by Badr-Eddine Benelmostafa, Rita Aitelhaj, Hicham Medromi

“…Our model introduces a novel version of Robust Feature Downsampling (RFD) method, enhanced with Convolutional Block Attention Modules (CBAM) and Normalization Perturbation (NP) to preserve key spatial information and improve feature discrimination across different weather conditions. …”
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