Deep Loving - The Friend of Deep Learning

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 201C;Deep Loving201D;. 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

Artificial Satisfaction - The Brother of Artificial Intelligence

John McCarthy (September 4, 1927 2013; October 24, 2011) was an American computer scientist and cognitive scientist. The term 201C;Artificial Intelligence201D; was coined by him (Wikipedia, 2020). Satish Gajawada (March 12, 1988 2013; Present) is an Indian Independent Inventor and Scientist. He coined the term 201C;Artificial Satisfaction201D; in this article (Gajawada, S., and Hassan Mustafa, 2019a). A new field titled 201C;Artificial Satisfaction201D; is introduced in this article. 201C;Artificial Satisfaction201D; will be referred to as 201C;The Brother of Artificial Intelligence201D; after the publication of this article. A new algorithm titled 201C;Artificial Satisfaction Algorithm (ASA)201D; 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 201C;Artificial Satisfaction Algorithm (ASA).201D; PSO and ASA algorithms 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 201C;Artificial Satisfaction Field201D; to the rest of the world rather than on implementing complex algorithms from scratch

Nature Plus Plus Inspired Computing - The Superset of Nature Inspired Computing

The term Nature Plus Plus Inspired Computing is coined by us in this article The abbreviation for this new term is N IC Just like the C programming language is a superset of C programming language Nature Plus Plus Inspired Computing N IC field is a superset of the Nature Inspired Computing NIC field We defined and introduced Nature Plus Plus Inspired Computing Field in this work Several interesting opportunities in N IC Field are shown for Artificial Intelligence Field Scientists and Students We show a literature review of the N IC Field after showing the definition of Nature Inspired Computing NIC Field The primary purpose of publishing this innovative article is to show a new path to NIC Field Scientists so that they can come up with various innovative algorithms from scratch As the focus of this article is to introduce N IC to researchers across the globe we added N IC Field concepts to the Particle Swarm Optimization algorithm and created the Children Cycle Riding Algorithm CCR Algorithm Finally results obtained by CCR Algorithm are shown followed by Conclusion

Testing Multiple Strategy Human Optimization based Artificial Human Optimization Algorithms

Recently a new trend titled ‘Artificial Human Optimization’ has become popular in Evolutionary Computing Domain. More than 30 papers were published in this new field proposed in December 2016. ‘Hassan Satish Particle Swarm Optimization (HSPSO)’ and ‘Human Inspired Differential Evolution (HIDE)’ are the two latest Artificial Human Optimization algorithms proposed based on Multiple Strategy Human Optimization. In this paper we focus on Testing HSPSO and HIDE by applying these latest algorithms on Ackley, Bohachevsky, Booth, Three-Hump Camel and Beale benchmark functions. Results obtained for these Artificial Human Optimization Algorithms are compared with Differential Evolution and Particle Swarm Optimization

An Artificial Human Optimization Algorithm titled Human Thinking Particle Swarm Optimization

Artificial Human Optimization is a latest field proposed in December 2016. Just like artificial Chromosomes are agents for Genetic Algorithms, similarly artificial Humans are agents for Artificial Human Optimization Algorithms. Particle Swarm Optimization is very popular algorithm for solving complex optimization problems in various domains. In this paper, Human Thinking Particle Swarm Optimization (HTPSO) is proposed by applying the concept of thinking of Humans into Particle Swarm Optimization. The proposed HTPSO algorithm is tested by applying it on various benchmark functions. Results obtained by HTPSO algorithm are compared with Particle Swarm Optimization algorithm.  &nbsp

On Analysis and Evaluation for Predicting Students’ Academic Performance GPA Considering an Engineering Institution (Neural Networks’ Modeling Approach)

Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results. Educational Institutions face numerous challenges today in providing quality and student-centric education to Students Individual learners prefer their own strategies originated from diverse learning styles. Learning style models may include collective strategies for mental, emotional, and physiological components. On the basis of such components, this piece of research suggests a specific quantified learning style preferred by learners in engineering education. By following average learners’ achievements (marks) at specific courses closely related to the specialization, interesting analytical results for Grade Point Average (GPA) evaluation are obtained. Moreover, an ANN model with supervised learning is presented to simulate diverse learning styles performance. Accordingly, optimal guided advise is suggested in fulfillment of probabilistically best GPA of graduated engineers. Obtained simulation results are well supported by the findings of experimental case study