...Abdulkarem H. M. Hussein Almawgani; Yahya Ali Abdelrahman Ali; Mohammed M. Alammar; Monir Abdullah Purpose This paper aims to study a highly sensitive multilayer surface plasmon resonance ( SPR ) biosensor with machine learning assistance; a dual-layer SPR sensor is proposed for brain lesion...

... on a hexagonal lattice comprising silicon rods suspended in air. Design/methodology/approach Rigorous simulations were conducted using OptiFDTD to optimize the structure. Conventional approaches often fail to capture certain variable parameters. Machine learning ( ML ) offers a robust method for identifying...

... w_donghui@163.com 21 01 2026 02 04 2026 15 04 2026 19 04 2026 © 2026 Emerald Publishing Limited 2026 Emerald Publishing Limited Licensed re-use rights only Data glove Deep learning EWCGR model Sign language recognition Machine learning Zhengzhou University...

... acquisition and preprocessing, data sets, feature extraction, machine learning models, DL, limitations, research direction, evaluation criteria and application scenarios. Propose a novel system-level analytical framework for this review that synthesizes electronic-nose literature by causally linking...

... analyzed by multiple methods, such as box plot, normality test and significance tests. Three typical machine learning algorithms were used to develop the classification models of risk-taking behavior. A deep learning algorithm was used to analyze time-series data derived from critical indicators...

... processing via coarse/fine-grained segmentation and combines signal processing (phase unwinding, smoothing) with machine learning to boost robustness. Its performance was tested by measuring recognition accuracy, response speed and anti-interference capability. Findings The RF-GRA system achieves 96.67...

... + PPG ) and machine learning-based artefact rejection (CNN–LSTM) enhance signal fidelity. However, most commercial wearables still rely on proprietary heuristics, limiting transparency and reproducibility. Cross-validation against ECG gold standards reveals that mean absolute percentage error...

... areas: the practical applications of PPG-derived metrics in athletic training and recovery; the multifaceted challenges to data fidelity, with a deep dive into motion artifacts and validation frameworks; and the evolution of signal processing and machine learning techniques for enhanced accuracy...

...Feng Liu; Zhen Liu; Xiang Zhang; Zongchen Liu; Yuan Liu Purpose This study aims to critically examine the integration of machine learning ( ML ) techniques into metal oxide ( MOX ) gas sensor arrays to address the challenges of poor selectivity and long-term drift. It analyzes the architecture...

...Zheng Sun; Lei Yin Purpose This study aims to critically evaluate recent advancements in intelligent textile sensors integrated with machine learning ( ML ) for continuous, non-invasive monitoring of athlete physiology. The focus is on enabling real-time, in-field assessment of biomechanical...

... to 82.5%, highlighting convective cooling loss as the primary error driver. Originality/value This work pioneers the integration of multi-physics CFD and machine learning for radiosonde error correction, achieving sub-Kelvin accuracy. By combining physical interpretability with the flexibility...

... Least squares method RBF neural network Electrochemical sensors Machine learning Cross-gas interference Chongqing research institute key project, 2023ZDYF03 and Tian Di technology innovation key project, 2024-TD-ZD013-01. As shown in Figure 1 , the coal mine safety monitoring system...

... into five thematic sections: fundamental principles, sport-by-sport applications, processing/validation methods, challenges and emerging directions (machine learning [ML], multimodal fusion and real-time feedback). Findings Across running, swimming, cycling, team, overhead, combat and niche sports, IMUs...

...Tulsi Pawan Fowdur; Ebrahim Muhammad Issack Boolaky; Sarvesh Sanjeevi Appadoo Purpose The purpose of this paper is to develop an IoT-based testbed for land displacement monitoring in real-time with blockchain-enabled transmission and machine learning for predictions. Cloud offloading has also been...

...Pooja Maurya; Sujan Yenuganti; Samatha Benedict; Dhananjay Budaraju Purpose This paper presents a cost-effective signal acquisition circuitry (SAC) for capturing surface electromyography (sEMG) data to classify different hand movements using advanced machine learning algorithms. The SAC...

... Emerald Publishing Limited Licensed re-use rights only Energy monitoring IoT Blockchain Machine learning Forecasting Security Throughput Blockchain is seen as the fifth disruptive innovation of the computing paradigm and it marks the fourth significant turning point in the evolution...

... drivers for such implementation. Design/methodology/approach The suggested prototype contains two main parts: hardware (low-cost components) and software (Machine Learning). An interconnection printed circuit board, a Raspberry Pi and a sensor chamber with the sensor array board make up the first part...

... to be reduced by a factor of six compared to baseline, which provides solid assets for smartphone implementation. Practical implications This work builds a bridge to already growing work in machine learning related to learning with small data set. Besides, the availability of systems that are able...

... and effects of MA on wearable monitoring systems is conducted. Also, a study from the literature on motion artifact detection and reduction is carried out and presented here. The benefits of a machine learning algorithm in a wearable monitoring system are also presented. Finally, distinct applications...

... olfactory system. Formaldehyde concentration prediction is one of the major functionalities of the E-nose, and three typical machine learning (ML) algorithms are most frequently used, including back propagation (BP) neural network, radial basis function (RBF) neural network and support vector regression...