Maximum entropy searching

Jiang, Rui; Zhou, Hui; Wang, Han; Ge, Shuzhi Sam

doi:10.1049/trit.2018.1058

Cited by 16 publications

(6 citation statements)

References 44 publications

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“…Usually, the sensitivity test is carried out for the performance evaluation of learners' learning style. e true positive cases are classified using equation (11). e comparison results of proposed AONN with existing fuzzy in each test can yield the suitable higher sensitivity results.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…e features of this system are useful in modeling, recognizing, and training daily activities. A new perspective of autonomous mobile robot path was presented by Jiang et al [11] to propose a biased direction towards casual entropy maximization. e context information is unfolded and computational cost is reduced by dilated convolutions in convolutional neural denoising network (ECNDNet) [12].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network

et al. 2020

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Widespread development of system software, the process of learning, and the excellence in profession of teaching are the formidable challenges faced by the learning behavior prediction system. The learning styles of teachers have different kinds of content designs to enhance their learning. In this learning environment, teachers can work together with the students, but the learning materials are designed by the teachers. The cognitive style deals with mental activities such as learning, remembering, thinking, and the usage of language. Therefore, being motivated by the problems mentioned above, this paper proposes the concept of adaptive optimization-based neural network (AONN). The learning behavior and browsing behavior features are extracted and incorporated into the input of artificial neural network (ANN). Hence, in this paper, the neural network weights are optimized with the use of grey wolf optimizer (GWO) algorithm. The output operation of e-learning with teaching equipment is chosen based on the cognitive style predicted by AONN. In experimental section, the measures of accuracy, sensitivity, specificity, time (sec), and memory (bytes) are carried out. Each of the measure is compared with the proposed AONN and existing fuzzy logic methodologies. Ultimately, the proposed AONN method produces higher accuracy, specificity, and sensitivity results. The results demonstrate that the algorithm proposed in this study can automatically learn network structures competitively, unlike those achieved for neural networks through standard approaches.

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Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network

et al. 2020

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“…In this section, the concern about the RL aims to find the optimal route in a dynamic environment based on a user's request. An agent is implemented in a cloud server, which makes use of the Markov Decision Process (MDP) to formulate the traffic sequences and to solve the decision‐making problem in order to find the optimal route in the dynamic situation 47,48 . In this algorithm, the requested user enters the destination location.…”

Section: Proposed Workmentioning

confidence: 99%

Optimized traffic flow prediction based on cluster formation and reinforcement learning

Rajkumar¹,

Deborah²,

Vijayakumar³

2019

Int J Communication

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In recent days, the traffic flow information is collected using the global positioning system through the Internet, which is yet to become ubiquitous. A novel technique is proposed for the intelligent transportation system, which leads to reduce the traffic congestion that will become an unavoidable phenomenon in the near future. This system uses a magnetic sensor to identify the type of the vehicle and the exact vehicle count in the traffic environment based on variation in the magnetic flux. This information is transmitted to the cloud server with the help of cluster by utilizing the nearby proximity services. An intelligent agent that uses reinforcement learning is implemented in the cloud server to learn the real-time traffic flow from multiple sources for the prediction of a valid and optimized route suggestion for the registered users. This work is implemented, and implementation results show that the proposed work achieves an accuracy of 98.36%. Hence, this intelligence method for VANETs will certainly account for improved traffic prediction to the vehicle transportation. It can reduce the vehicles waiting time in traffic and that would minimize the fuel consumption. It will make an eco-friendly environment of reduced carbon dioxide emissions in urban cities.

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“…Therefore, water movement analysis is essential to research on the mechanism behind water-conducting fracture zone development in overburden strata during mining. Many scholars have predicted the development of water-conducting fracture zones in coal mining [2][3][4][5][6][7][8][9][10]. Zhang [11] introduced a mechanism for water-conducting zone development and proposed improved measures in coal mining based on hydrogeological conditions.…”

Section: Introductionmentioning

confidence: 99%

Physical Simulation of the Water-Conducting Fracture Zone of Weak Roofs in Shallow Seam Mining Based on a Self-Designed Hydromechanical Coupling Experiment System

Zha

Liu

2020

Geofluids

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Due to inappropriate mining practices, water-conducting fracture zones can develop in an aquifer, not only destroying the surface-water environment but also causing water inrush, even hurting or killing workers. To avoid such disasters, investigating and simulating the evolution mechanism of water-conducting fractures are becoming a research focus in mining engineering, especially regarding the organisation and development of fractures. Our work mainly involved the design of low-strength analogous materials and the simulation of fracture evolution for weak-roof problems in shallow seam mining based on a self-built experimental hydromechanical coupling system. The experimental results show that the vertical stress in the roof increases first as the working face approaches and finally decreases to near its initial value as the working face passes. The relationship between fracture depth and coal-seam excavation distance is obviously nonlinear. The leakage velocity of surface water remains stable in the early stage of excavation and increases when the fracture develops through the main aquifuge. The maximum fracture depth is 76.18 m for the Yili coal mine with weak roofs and shallow coal seams. In addition, we numerically simulated and verified the evolution patterns with the FLAC3D platform. The simulated fracture depth of the Yili coal mine agreed with the in situ borehole observation very well and was more accurate than the output of the empirical formula. Our work provides new methods and relevant data for research on the evolution of water-conducting fractures in weak roofs during shallow seam mining.

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Maximum entropy searching

Cited by 16 publications

References 44 publications

Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network

Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network

Optimized traffic flow prediction based on cluster formation and reinforcement learning

Physical Simulation of the Water-Conducting Fracture Zone of Weak Roofs in Shallow Seam Mining Based on a Self-Designed Hydromechanical Coupling Experiment System

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