A Review on the Artificial Neural Network Approach to Analysis and Prediction of Seismic Damage in Infrastructure

Shokri, Manouchehr; Tavakoli, Kian

doi:10.1504/ijhm.2019.10026005

Cited by 11 publications

(11 citation statements)

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“…In this regard, clustering tends to be more effective in static networks, where network configuration remains the same for longer time. However, they need to be studied in dynamic as well as heterogeneous environments [ 132 – 136 ]. Finding an optimal location for these devices should be further investigated so that IoT can cater for a wide range of emerging healthcare applications in the years ahead.…”

Section: Challenges and Open Research Issuesmentioning

confidence: 99%

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

et al. 2021

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Section: Challenges and Open Research Issuesmentioning

confidence: 99%

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

et al. 2021

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“…Exploring multiple object locations, their scale, view orientation and the impact of scenery images are challenging tasks in the visual sensors [ 9 , 10 ] field. We have studied the literature in several domains such as multi-object categorization, object segmentation as well as labeling and scene classification in order to establish proper parameters and metrices for our proposed method.…”

Section: Related Workmentioning

confidence: 99%

A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression

Ahmed

Jalal

Kim

2020

Sensors

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In recent years, interest in scene classification of different indoor-outdoor scene images has increased due to major developments in visual sensor techniques. Scene classification has been demonstrated to be an efficient method for environmental observations but it is a challenging task considering the complexity of multiple objects in scenery images. These images include a combination of different properties and objects i.e., (color, text, and regions) and they are classified on the basis of optimal features. In this paper, an efficient multiclass objects categorization method is proposed for the indoor-outdoor scene classification of scenery images using benchmark datasets. We illustrate two improved methods, fuzzy c-mean and mean shift algorithms, which infer multiple object segmentation in complex images. Multiple object categorization is achieved through multiple kernel learning (MKL), which considers local descriptors and signatures of regions. The relations between multiple objects are then examined by intersection over union algorithm. Finally, scene classification is achieved by using Multi-class Logistic Regression (McLR). Experimental evaluation demonstrated that our scene classification method is superior compared to other conventional methods, especially when dealing with complex images. Our system should be applicable in various domains such as drone targeting, autonomous driving, Global positioning systems, robotics and tourist guide applications.

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“…In vision sensors, video cameras are fixed at surveillance locations to perform automated inspections of human activities. In recent years, these sensors have also become widely used in healthcare and fitness industries for researchers to improve the practices of user authentication [ 8 , 9 ]. Htike et al [ 10 ] proposed a posture-based HAR system using one static camera.…”

Section: Related Workmentioning

confidence: 99%

Wearable Inertial Sensors for Daily Activity Analysis Based on Adam Optimization and the Maximum Entropy Markov Model

Tahir

Jalal

Kim

2020

Entropy

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Advancements in wearable sensors technologies provide prominent effects in the daily life activities of humans. These wearable sensors are gaining more awareness in healthcare for the elderly to ensure their independent living and to improve their comfort. In this paper, we present a human activity recognition model that acquires signal data from motion node sensors including inertial sensors, i.e., gyroscopes and accelerometers. First, the inertial data is processed via multiple filters such as Savitzky–Golay, median and hampel filters to examine lower/upper cutoff frequency behaviors. Second, it extracts a multifused model for statistical, wavelet and binary features to maximize the occurrence of optimal feature values. Then, adaptive moment estimation (Adam) and AdaDelta are introduced in a feature optimization phase to adopt learning rate patterns. These optimized patterns are further processed by the maximum entropy Markov model (MEMM) for empirical expectation and highest entropy, which measure signal variances for outperformed accuracy results. Our model was experimentally evaluated on University of Southern California Human Activity Dataset (USC-HAD) as a benchmark dataset and on an Intelligent Mediasporting behavior (IMSB), which is a new self-annotated sports dataset. For evaluation, we used the “leave-one-out” cross validation scheme and the results outperformed existing well-known statistical state-of-the-art methods by achieving an improved recognition accuracy of 91.25%, 93.66% and 90.91% when compared with USC-HAD, IMSB, and Mhealth datasets, respectively. The proposed system should be applicable to man–machine interface domains, such as health exercises, robot learning, interactive games and pattern-based surveillance.

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A Review on the Artificial Neural Network Approach to Analysis and Prediction of Seismic Damage in Infrastructure

Cited by 11 publications

References 0 publications

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression

Wearable Inertial Sensors for Daily Activity Analysis Based on Adam Optimization and the Maximum Entropy Markov Model

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