HIMA 2011 committee workshop on hybrid intelligent models and applications (IEEE HIMA 2011)

2011 ◽  
Keyword(s):  
Intelligent Models

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

Recurrent Neural Networks for Edge Intelligence

2021 ◽  
Vol 54 (4) ◽  
pp. 1-38
Author(s):  
Varsha S. Lalapura ◽  
J. Amudha ◽  
Hariramn Selvamuruga Satheesh
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Model Performance ◽  
Network Architectures ◽  
Training Procedure ◽  
Resource Constrained ◽  
Compression Technique ◽  
Creative Art ◽  
Comprehensive Survey ◽  
Intelligent Models

Recurrent Neural Networks are ubiquitous and pervasive in many artificial intelligence applications such as speech recognition, predictive healthcare, creative art, and so on. Although they provide accurate superior solutions, they pose a massive challenge “training havoc.” Current expansion of IoT demands intelligent models to be deployed at the edge. This is precisely to handle increasing model sizes and complex network architectures. Design efforts to meet these for greater performance have had inverse effects on portability on edge devices with real-time constraints of memory, latency, and energy. This article provides a detailed insight into various compression techniques widely disseminated in the deep learning regime. They have become key in mapping powerful RNNs onto resource-constrained devices. While compression of RNNs is the main focus of the survey, it also highlights challenges encountered while training. The training procedure directly influences model performance and compression alongside. Recent advancements to overcome the training challenges with their strengths and drawbacks are discussed. In short, the survey covers the three-step process, namely, architecture selection, efficient training process, and suitable compression technique applicable to a resource-constrained environment. It is thus one of the comprehensive survey guides a developer can adapt for a time-series problem context and an RNN solution for the edge.

Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance

2019 ◽  
Vol 163 ◽  
pp. 907-926 ◽  
Author(s):  
Mohammed Falah Allawi ◽  
Othman Jaafar ◽  
Firdaus Mohamad Hamzah ◽  
Suhana Binti Koting ◽  
Nuruol Syuhadaa Binti Mohd ◽  
...  
Keyword(s):  
Operation Performance ◽  
Artificial Intelligent ◽  
Hydrological Parameters ◽  
Reservoir System ◽  
Intelligent Models

scholarly journals A Review and Comparison of the State-of-the-Art Techniques for Atrial Fibrillation Detection and Skin Hydration

2021 ◽  
Vol 2 ◽  
Author(s):  
Sidrah Liaqat ◽  
Kia Dashtipour ◽  
Adnan Zahid ◽  
Kamran Arshad ◽  
Sana Ullah Jan ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
State Of The Art ◽  
The State ◽  
Skin Hydration ◽  
Hydration Level ◽  
Independent Review ◽  
Intelligent Models ◽  
Electrocardiogram Ecg ◽  
Fast Heart Rate ◽  
Art Techniques

Atrial fibrillation (AF) is one of the most common types of cardiac arrhythmia, with a prevalence of 1–2% in the community, increasing the risk of stroke and myocardial infarction. Early detection of AF, typically causing an irregular and abnormally fast heart rate, can help reduce the risk of strokes that are more common among older people. Intelligent models capable of automatic detection of AF in its earliest possible stages can improve the early diagnosis and treatment. Luckily, this can be made possible with the information about the heart's rhythm and electrical activity provided through electrocardiogram (ECG) and the decision-making machine learning-based autonomous models. In addition, AF has a direct impact on the skin hydration level and, hence, can be used as a measure for detection. In this paper, we present an independent review along with a comparative analysis of the state-of-the-art techniques proposed for AF detection using ECG and skin hydration levels. This paper also highlights the effects of AF on skin hydration level that is missing in most of the previous studies.

Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

2018 ◽  
pp. 1-30 ◽  
Author(s):  
Jagan Jayabalan ◽  
Sanjiban Sekhar Roy ◽  
Pijush Samui ◽  
Pradeep Kurup
Keyword(s):  
Neural Network ◽  
Rock Mass ◽  
Elastic Modulus ◽  
Roughness Parameter ◽  
Jointed Rock ◽  
Jointed Rock Mass ◽  
Artificial Neural Network Ann ◽  
Learning Machine ◽  
Hidden Layer ◽  
Intelligent Models

Elastic Modulus (Ej) of jointed rock mass is a key parameter for deformation analysis of rock mass. This chapter adopts three intelligent models {Extreme Learning Machine (ELM), Minimax Probability Machine Regression (MPMR) and Generalized Regression Neural Network (GRNN)} for determination of Ej of jointed rock mass. MPMR is derived in a probability framework. ELM is the modified version of Single Hidden Layer Feed forward network. GRNN approximates any arbitrary function between the input and output variables. Joint frequency (Jn), joint inclination parameter (n), joint roughness parameter (r), confining pressure (s3) (MPa), and elastic modulus (Ei) (GPa) of intact rock have been taken as inputs of the ELM, GRNN and MPMR models. The output of ELM, GRNN and MPMR is Ej of jointed rock mass. In this study, ELM, GRNN and MPMR have been used as regression techniques. The developed GRNN, ELM and MPMR have been compared with the Artificial Neural Network (ANN) models.

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