Optimisation of propylene conversion response by neuro-fuzzy approach

Shahgoli, Aiyoub Fazli; Zandi, Yousef; Heirati, Arian; Khorami, Masoud; Mehrabi, Peyman; Petković, Dalibor

doi:10.1504/ijhm.2020.109918

Cited by 24 publications

(10 citation statements)

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“…Home care and community care can not only facilitate the elderly and reduce the burden of the family but also reduce the burden of medical staff [15]. is topic studies a set of intelligent telemedicine monitoring systems, which is used to monitor the elderly and patients with chronic diseases in the family and community [16], so that the guardians can monitor the basic situation of their own body at home and conduct remote video with the community medical staff [17], and at the same time, the medical staff of the community hospital can consult the health records of the guardians [18]. is system greatly reduces the burden of family care, facilitates the guardianship of the ward's health, and can be used for some chronic diseases [19].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Human Motion Representation and Motion Pattern Recognition Based on Complex Fuzzy Theory

Sun

2021

Complexity

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With the development of science and technology, the introduction of virtual reality technology has pushed the development of human-computer interaction technology to a new height. The combination of virtual reality and human-computer interaction technology has been applied more and more in military simulation, medical rehabilitation, game creation, and other fields. Action is the basis of human behavior. Among them, human behavior and action analysis is an important research direction. In human behavior and action, recognition research based on behavior and action has the characteristics of convenience, intuition, strong interaction, rich expression information, and so on. It has become the first choice of many researchers for human behavior analysis. However, human motion and motion pictures are complex objects with many ambiguous factors, which are difficult to express and process. Traditional motion recognition is usually based on two-dimensional color images, while two-dimensional RGB images are vulnerable to background disturbance, light, environment, and other factors that interfere with human target detection. In recent years, more and more researchers have begun to use fuzzy mathematics theory to identify human behaviors. The plantar pressure data under different motion modes were collected through experiments, and the current gait information was analyzed. The key gait events including toe-off and heel touch were identified by dynamic baseline monitoring. For the error monitoring of key gait events, the screen window is used to filter the repeated recognition events in a certain period of time, which greatly improves the recognition accuracy and provides important gait information for motion pattern recognition. The similarity matching is performed on each template, the correct rate of motion feature extraction is 90.2%, and the correct rate of motion pattern recognition is 96.3%, which verifies the feasibility and effectiveness of human motion recognition based on fuzzy theory. It is hoped to provide processing techniques and application examples for artificial intelligence recognition applications.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Human Motion Representation and Motion Pattern Recognition Based on Complex Fuzzy Theory

Sun

2021

Complexity

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“…Optimization is a process by which the optimal solutions of a problem that satisfies and objective function are accessed [ 106 , 107 , 108 , 109 ]. Yang, in [ 110 ], introduced an optimization algorithm inspired by a property of bats, known as echolocation.…”

Section: Proposed System Methodologymentioning

confidence: 99%

Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier

Abdullah

Ghadi

Gochoo

et al. 2021

Entropy

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To prevent disasters and to control and supervise crowds, automated video surveillance has become indispensable. In today’s complex and crowded environments, manual surveillance and monitoring systems are inefficient, labor intensive, and unwieldy. Automated video surveillance systems offer promising solutions, but challenges remain. One of the major challenges is the extraction of true foregrounds of pixels representing humans only. Furthermore, to accurately understand and interpret crowd behavior, human crowd behavior (HCB) systems require robust feature extraction methods, along with powerful and reliable decision-making classifiers. In this paper, we describe our approach to these issues by presenting a novel Particles Force Model for multi-person tracking, a vigorous fusion of global and local descriptors, along with a robust improved entropy classifier for detecting and interpreting crowd behavior. In the proposed model, necessary preprocessing steps are followed by the application of a first distance algorithm for the removal of background clutter; true-foreground elements are then extracted via a Particles Force Model. The detected human forms are then counted by labeling and performing cluster estimation, using a K-nearest neighbors search algorithm. After that, the location of all the human silhouettes is fixed and, using the Jaccard similarity index and normalized cross-correlation as a cost function, multi-person tracking is performed. For HCB detection, we introduced human crowd contour extraction as a global feature and a particles gradient motion (PGD) descriptor, along with geometrical and speeded up robust features (SURF) for local features. After features were extracted, we applied bat optimization for optimal features, which also works as a pre-classifier. Finally, we introduced a robust improved entropy classifier for decision making and automated crowd behavior detection in smart surveillance systems. We evaluated the performance of our proposed system on a publicly available benchmark PETS2009 and UMN dataset. Experimental results show that our system performed better compared to existing well-known state-of-the-art methods by achieving higher accuracy rates. The proposed system can be deployed to great benefit in numerous public places, such as airports, shopping malls, city centers, and train stations to control, supervise, and protect crowds.

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“…The max_pooling layer downsamples the result obtained by the convolution layer by using a sliding window of size 1 × 2. Thus the pooling results of the (p − 1)th layer, q kernel and x row and y column can be calculated as [55][56][57][58];…”

Section: Head Tracking Using Convolution Neural Networkmentioning

confidence: 99%

A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras

et al. 2021

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Automatic head tracking and counting using depth imagery has various practical applications in security, logistics, queue management, space utilization and visitor counting. However, no currently available system can clearly distinguish between a human head and other objects in order to track and count people accurately. For this reason, we propose a novel system that can track people by monitoring their heads and shoulders in complex environments and also count the number of people entering and exiting the scene. Our system is split into six phases; at first, preprocessing is done by converting videos of a scene into frames and removing the background from the video frames. Second, heads are detected using Hough Circular Gradient Transform, and shoulders are detected by HOG based symmetry methods. Third, three robust features, namely, fused joint HOG-LBP, Energy based Point clouds and Fused intra-inter trajectories are extracted. Fourth, the Apriori-Association is implemented to select the best features. Fifth, deep learning is used for accurate people tracking. Finally, heads are counted using Cross-line judgment. The system was tested on three benchmark datasets: the PCDS dataset, the MICC people counting dataset and the GOTPD dataset and counting accuracy of 98.40%, 98%, and 99% respectively was achieved. Our system obtained remarkable results.

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Optimisation of propylene conversion response by neuro-fuzzy approach

Cited by 24 publications

References 0 publications

[Retracted] Human Motion Representation and Motion Pattern Recognition Based on Complex Fuzzy Theory

[Retracted] Human Motion Representation and Motion Pattern Recognition Based on Complex Fuzzy Theory

Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier

A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras

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