scholarly journals New Trends in Artificial Intelligence: Applications of Particle Swarm Optimization in Biomedical Problems

2018 ◽  
Author(s):  
Aman Chandra Kaushik ◽  
Shiv Bharadwaj ◽  
Ajay Kumar ◽  
Avinash Dhar ◽  
Dongqing Wei
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization

scholarly journals Deep Loving - The Friend of Deep Learning

2020 ◽  
pp. 51-53
Author(s):  
Satish Gajawada ◽  
Hassan M. H. Mustafa
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Literature Review ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Definition Of

Artificial Intelligence and Deep Learning are good fields of research. Recently, the brother of Artificial Intelligence titled "Artificial Satisfaction" was introduced in literature [10]. In this article, we coin the term “Deep Loving”. After the publication of this article, "Deep Loving" will be considered as the friend of Deep Learning. Proposing a new field is different from proposing a new algorithm. In this paper, we strongly focus on defining and introducing "Deep Loving Field" to Research Scientists across the globe. The future of the "Deep Loving" field is predicted by showing few future opportunities in this new field. The definition of Deep Learning is shown followed by a literature review of the "Deep Loving" field. The World's First Deep Loving Algorithm (WFDLA) is designed and implemented in this work by adding Deep Loving concepts to Particle Swarm Optimization Algorithm. Results obtained by WFDLA are compared with the PSO algorithm.

An effective analysis of online education model based on artificial intelligence

2020 ◽  
pp. 1-12
Author(s):  
Lihua Peng
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Online Education ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Online Course ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Resource Generation

With the development of artificial intelligence in education, online education has been recognized by the society as a new teaching method. It can make full use of the advantages of the network across regions, and make full use of the advantages of network technology to share the resources of colleges and universities, which is a promising educational method. In response to the demand of online education for learner information, this paper proposes the learner model Neighbor Mean Variation Multi-Objective Particle Swarm Optimization-Genetic Algorithm (NMVMOPSO-GA). This model includes the learner’s learning interest sub-model, the learner’s cognitive ability sub-model and the learner’s knowledge sub-model. The modelling techniques of the three sub-models are discussed separately, and their status and role in the online education system are analyzed. At the same time, for the knowledge model that reflects the learner’s learning progress and knowledge mastery, a learner knowledge sub-model constructed with Bayesian networks is proposed. The neighbor mean mutation operator is introduced to optimize the multi-objective particle swarm optimization algorithm and improve the convergence performance and stability of the multi-objective particle swarm optimization algorithm. We study the application of multi-objective particle swarm optimization algorithm in online course resource generation service. Through simulation experiments, it is verified that the multi-objective particle swarm optimization algorithm can improve the performance and stability of online course resource generation.

Particle Swarm Optimization in Bioinformatics, Image Processing, and Computational Linguistics

2021 ◽  
Vol 12 (4) ◽  
pp. 25-44
Author(s):  
Badal Soni ◽  
Satashree Roy ◽  
Shiv Warsi
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Particle Swarm Optimization ◽  
Digital Image Processing ◽  
Computational Linguistics ◽  
Digital Image ◽  
Focal Point ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Engineering Problems

Since its inception, particle swarm optimization and its improvement has been an active area of research, and the algorithm has found its application in multifarious domains such as highly constrained engineering problems as well as artificial intelligence. The focal point of this paper is to make the reader aware of the innumerable applications of particle swarm optimization, especially in the field of bioinformatics, digital image processing, and computational linguistics. This review work is designed to serve as a comprehensive look-up guide and to navigate through the algorithm's scope and application in recent times in the aforementioned fields.

Particle swarm optimization neural network for research on artificial intelligence college English classroom teaching framework

2020 ◽  
pp. 1-13
Author(s):  
Dong Juan ◽  
Yu Hong Wei
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Personalized Learning ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
College English ◽  
Learning Path ◽  
English Classroom

This paper based on the algorithm of particle swarm optimization neural network, the university English classroom training framework with artificial intelligence is researched and designed, and a personalized learning path based on an improved binary particle swarm algorithm based on the non-linear increase of inertial weights and the exploration of unknown space is proposed. The recommendation method improves the algorithm’s convergence speed and convergence accuracy. It is easy to jump out of the local optimum through the improvement of the algorithm, thereby solving the problem of low recommendation accuracy of the personalized learning path and improving the recommendation efficiency. To verify the recommended effect of the model and algorithm, this paper designs a simulation experiment and a learning platform that take the college English course as an example to verify the running performance and practical application effect of the proposed method. The above experiments show that the proposed method can improve the matching degree of the personalized learning path and the needs of learners, and improve the accuracy of application in personalized learning path recommendation.

scholarly journals Artificial God Optimization – A Creation

2020 ◽  
Vol 7 (6) ◽  
pp. 01-10
Author(s):  
Satish Gajawada ◽  
Hassan Mustafa
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Complex Optimization ◽  
Primary Focus

Nature Inspired Optimization Algorithms have become popular for solving complex Optimization problems. Two most popular Global Optimization Algorithms are Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Of the two, PSO is very simple and many Research Scientists have used PSO to solve complex Optimization Problems. Hence PSO is chosen in this work. The primary focus of this paper is on imitating God who created the nature. Hence the term "Artificial God Optimization (AGO)" is coined in this paper. AGO is a new field which is invented in this work. A new Algorithm titled "God Particle Swarm Optimization (GoPSO)" is created and applied on various benchmark functions. The World's first Hybrid PSO Algorithm based on Artificial Gods is created in this work. GoPSO is a hybrid Algorithm which comes under AGO Field as well as PSO Field. Results obtained by PSO are compared with created GoPSO algorithm. A list of opportunities that are available in AGO field for Artificial Intelligence field experts are shown in this work.

scholarly journals Artificial Satisfaction - The Brother of Artificial Intelligence

2020 ◽  
pp. 29-36
Author(s):  
Satish Gajawada ◽  
Hassan M. H. Mustafa
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Computer Scientist ◽  
Swarm Optimization ◽  
John Mccarthy ◽  
The World ◽  
Cognitive Scientist

John McCarthy (September 4, 1927 – October 24,2011) was an American computer scientist and cognitive scientist. The term “Artificial Intelligence” was coined by him(Wikipedia, 2020). Satish Gajawada (March 12, 1988 – Present) is an Indian Independent Inventor and Scientist. He coined the term “Artificial Satisfaction” in this article (Gajawada, S., and Hassan Mustafa, 2019a). A new field titled “Artificial Satisfaction” is introduced in this article. “Artificial Satisfaction” will be referred to as “The Brother of Artificial Intelligence” after the publication of this article. A new algorithm titled “Artificial Satisfaction Algorithm (ASA)” is designed and implemented in this work. For the sake of simplicity, Particle Swarm Optimization (PSO) Algorithm is modified with Artificial Satisfaction Concepts to create the“Artificial Satisfaction Algorithm (ASA).” PSO and AS Aalgorithms are applied on five benchmark functions. A comparision is made between the results obtained. The focus of this paper is more on defining and introducing “Artificial Satisfaction Field” to the rest of the world rather than on implementing complex algorithms from scratch.

scholarly journals MSSA-PSO: A Hybrid Technique

2020 ◽  
Vol 9 (4) ◽  
pp. 1786-1795
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Particle Swarm ◽  
Hybrid Technique ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

This paper center a multi objective based half and half procedure to fathom EED (Economic Emission Dispatch) issue incorporates wind power with hydro-warm units. The half breed procedure is the joined execution of both the modified salp swarm streamlining algorithm (MSSA) with counterfeit astute AI (artificial intelligence) strategy helped with particle swarm optimization (PSO) system. In this, the MSSA is used to advancing the blend of the warm generators dependent on the breeze power vulnerability and siphoned stockpiling units. PSO-ANN is used to catch the vulnerability occasions of wind power so the framework is guaranteed the high use of wind power. Along these lines, arrangement of the proposed enhancement approach will be limited the all out expense. To approve the proposed technique viability, the six and ten producing units warm framework is contemplated with fuel and discharge cost as two clashing targets to be upgraded simultaneously. The proposed strategy is executed in MATLAB working stage and the outcomes will be analyzed with thinking about age units and will contrasted with IMFO-RNN systems. The correlation comes about uncovers the nature of the proposed approach and broadcasts its capacity for dealing with multi-target improvement issues of intensity frameworks.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sign in / Sign up

Export Citation Format

Share Document