Engine Speed Reduction for Hydraulic Machinery Using Predictive Algorithms

Wiens, Travis

doi:10.1504/ijhm.2019.10019496

Cited by 33 publications

(36 citation statements)

References 3 publications

(3 reference statements)

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“…These networks support a shared weight architecture and are used to analyze visual imagery. 5,6,46,47 The layers of CNN and weight sharing features are de¯ned as follows:…”

Section: Deep Learning Modelmentioning

confidence: 99%

Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images

Singh

Kumar

Yadav

et al. 2020

Int. J. Patt. Recogn. Artif. Intell.

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There are limited coronavirus disease 2019 (COVID-19) testing kits, therefore, development of other diagnosis approaches is desirable. The doctors generally utilize chest X-rays and Computed Tomography (CT) scans to diagnose pneumonia, lung inflammation, abscesses, and/or enlarged lymph nodes. Since COVID-19 attacks the epithelial cells that line our respiratory tract, therefore, X-ray images are utilized in this paper, to classify the patients with infected (COVID-19 [Formula: see text]ve) and uninfected (COVID-19 [Formula: see text]ve) lungs. Almost all hospitals have X-ray imaging machines, therefore, the chest X-ray images can be used to test for COVID-19 without utilizing any kind of dedicated test kits. However, the chest X-ray-based COVID-19 classification requires a radiology expert and significant time, which is precious when COVID-19 infection is increasing at a rapid rate. Therefore, the development of an automated analysis approach is desirable to save the medical professionals’ valuable time. In this paper, a deep convolutional neural network (CNN) approach is designed and implemented. Besides, the hyper-parameters of CNN are tuned using Multi-objective Adaptive Differential Evolution (MADE). Extensive experiments are performed by considering the benchmark COVID-19 dataset. Comparative analysis reveals that the proposed technique outperforms the competitive machine learning models in terms of various performance metrics.

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“…These networks support a shared weight architecture and are used to analyze visual imagery. 5,6,46,47 The layers of CNN and weight sharing features are de¯ned as follows:…”

Section: Deep Learning Modelmentioning

confidence: 99%

Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images

Singh

Kumar

Yadav

et al. 2020

Int. J. Patt. Recogn. Artif. Intell.

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“…The MSS algorithm searches for the highest concentration of similar pixels space in the sample image and estimates the local density of pixels. MSS then performs density estimation iteratively and finds the minimum local value for density [ 31 ] so that all pixels having local density near to local minimum density are easily shifted to clusters of similar attributes (see Figure 5 ). This is a non-parametric clustering technique which does not depend on any prior knowledge of the objects or picture elements.…”

Section: Overview Of Solution Frameworkmentioning

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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“…Walsh–Hadamard transform (WHT) is used as an orthogonal transformation that splits our inertial signal into a set of signals [ 25 ]. Then, it finds dense property (i.e., energy of these signals), which deals with the real numbers, helps to minimize the computational costs [ 26 ] and produces a more robust set of features. Figure 7 represents the sensor fusion of an accelerometer and gyroscope with motion patterns of walking forward activity via WHT, respectively.…”

Section: Designed Framework For Wearable Harmentioning

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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Engine Speed Reduction for Hydraulic Machinery Using Predictive Algorithms

Cited by 33 publications

References 3 publications

Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images

Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images

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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