Artificial Neural Network based Particle Swarm Optimization solution approach for the inverse depletion of used nuclear fuel

Real-Time Image Processing and Deep Learning 2023

2023

The COVID-19 epidemic forced governments to adopt worldwide lockdowns in order to limit the virus's spread. Wearing a face mask, it is said, would reduce the possibility of transmission. Due to the growing urban population, proper city management is more important than ever in the modern day to reduce the impacts of COVID-19 infection. To check the mask in public places, however, would require incredibly long lineups and delays. Therefore, it is necessary for an autonomous mask detection system to assess whether someone is wearing a face mask. On the face mask dataset, three different machine learning methods are applied to determine the likelihood of wearing a face mask. The models were assessed using a number of measures, including accuracy, recall, and ROC curve. The main objective of the study is to detect the presence of face masks using deep learning, machine learning, and image processing approaches. All three models-NB, KNN, and CNN-achieved noteworthy accuracy of more than 80%, with CNN showing the best overall performance.

Section: Machine Learningmentioning

confidence: 99%

Face mask detection using machine learning

Eladham

Nassif

Real-Time Image Processing and Deep Learning 2023

2023

“…The chameleon is a kind of reptile renowned for its extraordinary capacity to alter its color to blend into its environment [1]. The Chameleon Swarm Algorithm (CSA) similar to PSO [2][3][4][5][6] is a metaheuristic nature-inspired optimization method created to address engineering optimization issues, which was inspired by the chameleon's hunting activity [7]. There are three key phases to the algorithm: tracking, pursuit, and attack.…”

Section: Literature Review 21 Chameleon Swarm Algorithmmentioning

confidence: 99%

Path planning and obstacle avoidance utilizing chameleon swarm algorithm

Ballous

Unmanned Systems Technology XXV

Nassif

et al. 2023

Path planning and obstacle avoidance are crucial tasks in the robotics and autonomous industry. Path planning seeks to determine the most efficient path between a start and an end point, whereas obstacle avoidance seeks to avoid collisions with static or dynamic obstacles in the environment. On this work, we utilize the Chameleon Swarm Algorithm (CSA), which is a metaheuristic approach, for path planning and obstacle avoidance on a predetermined map with static obstacles. This CSA extracted the optimal path from several possible different paths, and the results showed that it has slightly superior performance compared to PSO.

“…Moreover, the usage of the machine learning could you help losing weight and improve the life quality [13][14][15][16][17][18][19][20][21][22][23]. Moreover, metaheuristic algorithms, integrated with machine learning techniques [24][25][26][27][28][29][30][31][32][33][34][35][36], can optimize the selection of input parameters for WBV studies on weight loss and quality of life improvements, overcoming challenges related to standardized protocols and diverse parameter settings, and providing more consistent and reliable outcomes [37][38][39][40][41][42][43][44][45][46][47][48][49]. In this work, the work of [1,12] is extended to cover the effect of the human gender on the apparent mass.…”

Section: Introductionmentioning

confidence: 99%

“…The human age, weight, body mass index (BMI), height, and the frequency level, all beside the gender are used to study the BioR of the subjects while exited by a vibration platform. Beside the many usages [44,[50][51][52][53][54][55][56][57][58][59], previously, the transmissibility in all three directions were modeled using Artificial Neural Network (ANN). However, up to the knowledge of the authors, the effect on the apparent mass was not studied so far.…”

Section: Introductionmentioning

confidence: 99%

Studying the gender effect on apparent mass during whole-body vibration using ANN

Smart Biomedical and Physiological Sensor Technology XX

Nawayseh

2023

Artificial Neural Network (ANN) is a powerful tool to model a system using only the inputs and outputs of that system. In this paper, ANN is used to model the relation between the subject's gender to its performance while been excited in a whole-body vibration machine (WBV). For training the ANN, 20 male and 20 female subjects were observed during an experimental setup using a WBV at different vibration frequencies in the range of 20 to 45 Hz. The apparent mass was measured for the subjects at different frequencies. The input to the ANN includes body mass index, mass, and gender of the subjects along with and the excitation frequency. The ANN shows a good performance and extract the relationship with a performance that has a root mean squared error of the relative percentage error less than 9%.