Classification Studies on Vibrational Patterns of Distributed Fiber Sensors using Machine Learning

Pranay, Yada Sai; Tabjula, Jagadeeshwar; Kanakambaran, Srijith

doi:10.1109/ibssc56953.2022.10037519

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…The validity quadrants for various topics in optical fiber sensing and machine learning research are shown in Figure 8. The most commonly used term in quadrant I is optical fiber sensor, which refers to the use of optical fiber technology to implement various solutions for measuring chemical and physical properties such as temperature [64,[81][82][83][84], strain [44,53,[85][86][87], refractive index [88][89][90][91][92][93][94], curvature [70,71], vibration [72], and so on. Similarly, additional terms such as structural health monitoring, deep learning, FBGs, and leak detection have been identified as the most frequently used terms in the last three years.…”

Section: Analysis Of Emerging Wordsmentioning

confidence: 99%

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Reyes-Vera,

Valencia-Arias,

García-Pineda

et al. 2024

Sensors

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The constant monitoring and control of various health, infrastructure, and natural factors have led to the design and development of technological devices in a wide range of fields. This has resulted in the creation of different types of sensors that can be used to monitor and control different environments, such as fire, water, temperature, and movement, among others. These sensors detect anomalies in the input data to the system, allowing alerts to be generated for early risk detection. The advancement of artificial intelligence has led to improved sensor systems and networks, resulting in devices with better performance and more precise results by incorporating various features. The aim of this work is to conduct a bibliometric analysis using the PRISMA 2020 set to identify research trends in the development of machine learning applications in fiber optic sensors. This methodology facilitates the analysis of a dataset comprised of documents obtained from Scopus and Web of Science databases. It enables the evaluation of both the quantity and quality of publications in the study area based on specific criteria, such as trends, key concepts, and advances in concepts over time. The study found that deep learning techniques and fiber Bragg gratings have been extensively researched in infrastructure, with a focus on using fiber optic sensors for structural health monitoring in future research. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. This presents an opportunity for future studies.

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Section: Analysis Of Emerging Wordsmentioning

confidence: 99%

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Reyes-Vera,

Valencia-Arias,

García-Pineda

et al. 2024

Sensors

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“…The K Neighbors Classifier (KNC) method [27], [28] operates by identifying the proximity of data points in a feature space, classifying each point based on the labels of its knearest neighbors. In diagnosing Down Syndrome in children using facial images, the KNC leverages the facial features extracted from images to create a multidimensional representation.…”

Section: F K Neighbors Classifiermentioning

confidence: 99%

Novel Transfer Learning Based Deep Features for Diagnosis of Down Syndrome in Children Using Facial Images

Raza,

Munir,

Almutairi

et al. 2024

IEEE Access

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Down syndrome is a chromosomal condition characterized by the existence of an additional copy of chromosome 21. This genetic anomaly leads to a range of developmental challenges and distinct physical characteristics in affected children. Children with Down syndrome often exhibit specific craniofacial proportions, such as a relatively shorter midface and broader facial width. These distinct facial features, including a flat nasal bridge, almond-shaped eyes, and a small and somewhat flattened head, can serve as valuable indicators for early diagnosis and intervention. This study aims at the early diagnosis of Down syndrome using an advanced neural network approach. We used 3,009 facial images of children with Down syndrome and healthy children taken from the age group range of 0 to 15 for conducting our research experiments. We proposed a novel transfer learning-based feature generation named VNL-Net, which is an ensemble of VGG16, Non-Negative Matrix Factorization (NMF), and Light Gradient Boosting Machine (LGBM) methods. This unique VNL-Net feature extraction initially extracts spatial features from input image data. Then, the ensemble feature set of NMF and LGBM is extracted from spatial features. We built several advanced artificial intelligence-based approaches on the newly created feature set to evaluate performance. Extensive research experimental results show that the logistic regression method outperformed state-of-the-art studies with a high-performance accuracy of 0.99. We also fine-tuned each applied method and validated performance using the k-fold cross-validation mechanism. The runtime computational complexity of the applied methods is also determined. Our proposed innovative research has the ability to revolutionize the early diagnosis of Down syndrome in children using facial images.

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Classification Studies on Vibrational Patterns of Distributed Fiber Sensors using Machine Learning

Cited by 2 publications

References 8 publications

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Novel Transfer Learning Based Deep Features for Diagnosis of Down Syndrome in Children Using Facial Images

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