High-quality fluorescence imaging of biological systems is limited by processes like photobleaching and phototoxicity, and also in many cases, by limited access to the latest generations of microscopes. Moreover, low ...
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Facial expression recognition is one of the fields that nowadays has attracted the attention of many researchers. It is possible to automate facial expression recognition using artificial intelligence methods. This wi...
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ISBN:
(纸本)9798350398830
Facial expression recognition is one of the fields that nowadays has attracted the attention of many researchers. It is possible to automate facial expression recognition using artificial intelligence methods. This will be of great help to researchers, especially in areas such as psychology. Automatic facial recognition can be derived from a static image of facial expression, but a better and more efficient way to do this is through a sequence of images. In this paper, a new method is proposed to automatically detect facial expressions from a sequence of images. Each sequence of facial images begins with a face neutral state and ends with one of the six main emotions. Motion vectors are extracted from the sequence using optical flow algorithm. These vectors are then used to train the conditional random field and finally to automatically determine the emotion. In this paper, in addition to the basic conditional random field, the hidden dynamic conditional random field is also investigated. Additionally, the effect of changing some parameters of these algorithms such as different optimization methods has been investigated. Given that a facial expression is recognized during a sequence of images, random field-based methods can be used for efficient classification of facial expressions reaching accuracy (more than 90%) competitive with the best existing methods for facial expression recognition.
Mechanical straining-induced bandgap modulation in two-dimensional (2D) materials has been confined to volatile and narrow modulation due to substrate slippage and poor strain transfer. We report the thermomechanical ...
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In this paper we consider Bayesian parameter inference associated to a class of partially observed stochastic differential equations (SDE) driven by jump processes. Such type of models can be routinely found in applic...
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Device degradation due to hot carrier injection (HCI) in different Y-gate HEMT devices is thoroughly analyzed. To further understand the HCI reliability of the Y-gate HEMT devices, the device is fabricated with AlGaN/...
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Biomedical datasets distill many mechanisms of human diseases, linking diseases to genes and phenotypes (signs and symptoms of disease), genetic mutations to altered protein structures, and altered proteins to changes...
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Foreign Exchange market is the world's largest daily currency turnover. Two of the popular currencies Euro and Pound sterling traded against the US Dollar. Since the Russia and Ukraine war started in February 2022...
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ISBN:
(纸本)9798350345728
Foreign Exchange market is the world's largest daily currency turnover. Two of the popular currencies Euro and Pound sterling traded against the US Dollar. Since the Russia and Ukraine war started in February 2022, their exchange rates decrease to the lowest rate ever. Even though the general trend is bearish, several daily candles increase for some days making challenges for forex analysts. To solve this problem, classification is applied. The data is labeled downward and upward. By utilizing Linear Kernel and Radial Basis Function (RBF) Kernel-based Support Vector Machines (SVM), the candle direction can be classified and optimized by tuning the Hyperparameters. The accuracy of candle direction classifications are highly improved. After tuning, in general, classification using Linear Models can outperform RBF Models. The best accuracy found on the Pound sterling against US Dollar by using the Linear model is 98.11% and the accuracy becomes 100% on data testing at a ratio of 70:30. Whilst for the Euro against the US Dollar, the best accuracy found the same for both Linear and RBF models on a ratio of 80:20 at 97.53%. However, on data testing, it decreases to 94.51% for Linear Model and 93.41% using RBF Model. The implication of this study is SVM can successfully classify candle direction on pairs in the Forex Market that are affected by a big event that comes for such a long period as long as the hyperparameter is tuned.
The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but...
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Many data-driven patient risk stratification models have not been evaluated prospectively. We performed and compared the prospective and retrospective evaluations of 2 Clostridioides difficile infection (CDI) risk-pre...
Many data-driven patient risk stratification models have not been evaluated prospectively. We performed and compared the prospective and retrospective evaluations of 2 Clostridioides difficile infection (CDI) risk-prediction models at 2 large academic health centers, and we discuss the models’ robustness to data-set shifts.
Significant progress has been made in the development of an edge computing system for the fusion, representation, and visualization of dual-sensor data for obstacle avoidance control of mobile robots. This involves th...
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ISBN:
(纸本)9781665494052
Significant progress has been made in the development of an edge computing system for the fusion, representation, and visualization of dual-sensor data for obstacle avoidance control of mobile robots. This involves the use of an error-filtering covariance and averaging algorithm to logically fuse distance measurements from a pair of infrared and ultrasonic sensors, which was instrumented into a robot and used to generate radar visuals to track the proximity of ambient obstacles within 180 degrees spanning ahead of the robot. Hands-on experiments were performed to evaluate the performance and applicability of the system in real-time. The results show that the developed system is viable and robust. In line with the emerging field of edge computing, this work is an efficient, portable, and cost-effective approach to developing mobile robotic systems.
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