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[17] viXra:2304.0215 [pdf] submitted on 2023-04-26 06:09:28
Authors: Satish Gajawada, Hassan Mustafa
Comments: 18 Pages.
The term "Artificial Human Optimization" was first coined by the corresponding author of this work in December 2016 when he published a paper titled "Entrepreneur : Artificial Human Optimization" at Transactions on Machine Learning and Artificial Intelligence (TMLAI) Volume 4, No 6 (December 2016). According to that paper published in 2016, Artificial Human Optimization Field is defined as the collection of all those optimization algorithms which were proposed based on Artificial Humans. In real world we (Humans) solve the problems. In the same way Artificial Humans imitate real Humans in the search space and solve the optimization problems. In Particle Swarm Optimization (PSO) the basic entities in the solution space are Artificial Birds whereas in Artificial Human Optimization the basic entities in search space are Artificial Humans. Each Artificial Human corresponds to a point in the solution space. Ten Artificial Human Optimization methods titled "Human Bhagavad Gita Particle Swarm Optimization (HBGPSO)", "Human Poverty Particle Swarm Optimization (HPPSO)", "Human Dedication Particle Swarm Optimization (HuDePSO)", "Human Selection Particle Swarm Optimization (HuSePSO)", "Human Safety Particle Swarm Optimization (HuSaPSO)", "Human Kindness Particle Swarm Optimization (HKPSO)", "Human Relaxation Particle Swarm Optimization (HRPSO)", "Multiple Strategy Human Particle Swarm Optimization (MSHPSO)", "Human Thinking Particle Swarm Optimization (HTPSO)", "Human Disease Particle Swarm Optimization (HDPSO)" are applied on various benchmark functions and results obtained are shown in this work.
Category: Artificial Intelligence
[16] viXra:2304.0214 [pdf] submitted on 2023-04-26 06:16:58
Authors: Satish Gajawada, Hassan Mustafa
Comments: 9 Pages.
The Soul is eternal and exists even after death of a person or animal. The main idea that is captured in this work is that soul continues to exist and takes a different body after the death. The primary goal of this work is to invent a new field titled "Artificial Soul Optimization (ASO)". The term "Artificial Soul Optimization" is coined in this paper. All the Optimization algorithms which are proposed based on Artificial Souls will come under "Artificial Soul Optimization" Field (ASO Field). In the Particle Swarm Optimization and Artificial Human Optimization, the basic entities in search space are Artificial Birds and Artificial Humans respectively. Similarly, in Artificial Soul Optimization, the basic entities in search space are Artificial Souls. In this work, the ASO Field concepts are added to Particle Swarm Optimization (PSO) algorithm to create a new hybrid algorithm titled "Soul Particle Swarm Optimization (SoPSO). The proposed SoPSO algorithm is applied on various benchmark functions. Results obtained are compared with PSO algorithm. The World's first Hybrid PSO algorithm based on Artificial Souls is created in this work.
Category: Artificial Intelligence
[15] viXra:2304.0213 [pdf] submitted on 2023-04-26 06:25:46
Authors: Satish Gajawada, Hassan Mustafa
Comments: 8 Pages.
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 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 "Artificial Satisfaction Field" to the rest of the world rather than on implementing complex algorithms from scratch.
Category: Artificial Intelligence
[14] viXra:2304.0212 [pdf] submitted on 2023-04-26 06:36:20
Authors: Satish Gajawada, Hassan Mustafa
Comments: 5 Pages.
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.
Category: Artificial Intelligence
[13] viXra:2304.0211 [pdf] submitted on 2023-04-26 06:43:47
Authors: Satish Gajawada, Hassan Mustafa
Comments: 5 Pages.
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 Conclusions.
Category: Artificial Intelligence
[12] viXra:2304.0210 [pdf] submitted on 2023-04-26 06:54:03
Authors: Satish Gajawada, Arun Kumar, Maria Celestina Vanaja, Baby Supriya Sri Valikala
Comments: 4 Pages.
Artificial Neural Networks Field (ANN Field) is an exciting field of research. ANN field took its inspiration from Human Brain. The heart and Brain are very important for the survival of Humans. Research Scientists published many articles by giving importance to Brain. But scientists have not yet explored much on the Heart which is another important part in addition to the Brain. The primary purpose of publishing this article is to show a path to ANN field Research Scientists by introducing the concept of "Heart" into Artificial Neural Networks. In this paper, we coined and defined "Artificial Heart Neuron", which is the basic part of Artificial Heart Neural Networks Field (AHNN Field) in addition to Artificial Neuron. This work takes its inspiration from both Heart and Brain.
Category: Artificial Intelligence
[11] viXra:2304.0203 [pdf] submitted on 2023-04-25 09:04:30
Authors: Satish Gajawada, Hassan Mustafa
Comments: 11 Pages.
The main purpose of writing this article is to unify all the OUT OF THE BOX ideas (under Artificial Intelligence) invented by the corresponding author of this work during the period (2013-2022) under a single umbrella titled "Out of the BOX Artificial Intelligence Field (OBAI Field)". All the OUT OF THE BOX ideas which are proposed under Artificial Intelligence will come under new field titled OBAI Field which is defined in this work. A new Artificial Intelligence field titled "Artificial Cartoon Algorithms (ACA)" is invented in this work. ACA is a sub-field of OBAI field as it is an OUT OF THE BOX idea. Four new algorithms titled "Artificial Cartoon Popeye Algorithm", "Artificial Cartoon Chhota Bheem Algorithm", "Artificial Cartoon Jerry Algorithm" and "Artificial Cartoon Happy Kid Algorithm" are designed in this work.
Category: Artificial Intelligence
[10] viXra:2304.0202 [pdf] submitted on 2023-04-25 09:12:01
Authors: Satish Gajawada, Hassan Mustafa
Comments: 8 Pages.
A new field titled "The Interesting and Complete Artificial Intelligence (ICAI)" is invented in this work. In this article, we define this new ICAI field. Four new ICAI algorithms are designed in this work. This paper titled "The Interesting and Complete Artificial Intelligence (ICAI) — Version 1" is just the starting point of this new field. We request Research Scientists across the globe to work in this new direction of Artificial Intelligence and publish their work with titles such as "The Interesting and Complete Artificial Intelligence (ICAI) — Version 1.1", "The Interesting and Complete Artificial Intelligence (ICAI) — Version 2" or "The Interesting and Complete Artificial Intelligence (ICAI) — Final Version".
Category: Artificial Intelligence
[9] viXra:2304.0201 [pdf] submitted on 2023-04-25 09:18:08
Authors: Satish Gajawada, Hassan Mustafa
Comments: 12 Pages.
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.
Category: Artificial Intelligence
[8] viXra:2304.0200 [pdf] submitted on 2023-04-25 09:27:48
Authors: Satish Gajawada
Comments: 8 Pages.
Artificial Excellence is a new field which is invented in this article. Artificial Excellence is a new field which belongs to Artificial Human Optimization field. Artificial Human Optimization is a sub-field of Evolutionary Computing. Evolutionary Computing is a sub-field of Computational Intelligence. Computational Intelligence is an area of Artificial Intelligence. Hence after the publication of this article Artificial Excellence (AE) will become popular as a new branch of Artificial Intelligence (AI). A new algorithm titled Artificial Satish Gajawada and Durga Toshniwal Algorithm (ASGDTA) is designed in this work. The definition of AE is given in this article followed by many opportunities in the new AE field. The Literature Review of Artificial Excellence field is shown after showing the definition of Artificial Intelligence. The new ASGDTA Algorithm is explained followed by Results and Conclusions.
Category: Artificial Intelligence
[7] viXra:2304.0199 [pdf] submitted on 2023-04-25 09:34:17
Authors: Satish Gajawada, Hassan Mustafa
Comments: 3 Pages.
In this letter we coined, invented and defined a new branch titled "Artificial Intelligence Plus Plus (AI++)".
Category: Artificial Intelligence
[6] viXra:2304.0130 [pdf] submitted on 2023-04-18 15:47:19
Authors: Yew Kee Wong, Yifan Zhou, Yan Shing Liang, Haichuan Qiu
Comments: 9 Pages.
The Research & Development (R&D) phase of drug development is a lengthy and costly process. To revolutionize this process, we introduce our new concept QMLS to shorten the whole R&D phase to three to six months and decrease the cost to merely fifty to eighty thousand USD. For Hit Generation, Machine Learning Molecule Generation (MLMG) generates possible hits according to the molecular structure of the target protein while the Quantum Simulation (QS) filters molecules from the primary essay based on the reaction and binding effectiveness with the target protein. Then, For Lead Optimization, the resultant molecules generated and filtered from MLMG and QS are compared, and molecules that appear as a result of both processes will be made into dozens of molecular variations through Machine Learning Molecule Varication (MLMV), while others will only be made into a few variations. Lastly, all optimized molecules would undergo multiple rounds of QS filtering with a high standard for reaction effectiveness and safety, creating a few dozen pre-clinical-trail-ready drugs. This paper is based on our first paper [1], where we pitched the concept of machine learning combined with quantum simulations. In this paper we will go over the detailed design and framework of QMLS, including MLMG, MLMV, and QS.
Category: Artificial Intelligence
[5] viXra:2304.0129 [pdf] submitted on 2023-04-18 15:49:54
Authors: Yew Kee Wong, Yifan Zhou, Yan Shing Liang, Hai Chuan Qiu, Yu Xi Wu, Bin He
Comments: 13 Pages.
The Research & Development (R&D) phase of drug development is a lengthy and costly process, usually spanning from six to nine years [1] and costing four hundred to fourteen hundred million USD [2]. To revolutionize this process, we introduce our new concept-the combination of Quantum-based Machine Learning network (QML) and Quantum Computing Simulation (QS)-to shorten the whole R&D phase to three to six months and decrease the cost to merely fifty to eighty thousand USD. Our program takes the inputs of the target protein/gene structure and the primary essay [3]. For Hit Generation [3], the QML network generates possible hits [4] according to the molecular structure of the target protein while the QS filters molecules from the primary essay based on the reaction and binding effectiveness with the target protein. Then, For Lead Optimization [3], the resultant molecules generated and filtered from QML and QS are compared, and the ones that appear as a result of both processes will be made into dozens of molecular variations, while others will only undergo simple modifications. Lastly, all optimized molecules would undergo multiple rounds of QS filtering with a high standard for reaction effectiveness and safety, creating a few dozen pre-clinical-trail-ready drugs. Our concept of the combination of QML and QS can also prove revolutionary in many other fields, such as agriculture research, genetic editing, and even aerospace engineering.
Category: Artificial Intelligence
[4] viXra:2304.0089 [pdf] replaced on 2023-06-09 00:50:28
Authors: Friedrich Sösemann
Comments: 12 pages english, 12 pages german
Information, knowledge and intelligence are defined as a hierarchy of relations:Information as dependent properties, knowledge as dependent information, and intelligence as dependent knowledge. The same dependency measure applies to all three.Syntax, semantics and pragmatics of descriptions embody information, knowledge and intelligence.The precision and measurability of these terms should reduce vagueness and contradictions in their application.
Category: Artificial Intelligence
[3] viXra:2304.0037 [pdf] submitted on 2023-04-06 00:21:35
Authors: G. Tolimalu
Comments: 1 Page. In Japanese
The author proposes an idea for a new Internet bulletin board.
Category: Artificial Intelligence
[2] viXra:2304.0035 [pdf] submitted on 2023-04-05 00:36:52
Authors: G. Tolimalu
Comments: 2 Pages.
I will explain why the approach of learning a large amount of natural language does not contribute to the improvement of true AI intelligence, and why an alternative approach is required, in the form of a contrast between the mainstream and the author's views.
Category: Artificial Intelligence
[1] viXra:2304.0003 [pdf] submitted on 2023-04-01 16:03:19
Authors: Thiago M. Nóbrega
Comments: 8 Pages.
Computational consciousness is a novel hypothesis that aims to repli-cate human consciousness in artificial systems using Multithreaded Prior-ity Queues (MPQs) and machine learning models. The study addressesthe challenge of processing continuous data from various categories, suchas vision, hearing, and speech, to create a coherent and context-aware sys-tem. The proposed model employs parallel processing and multithreading,allowing multiple threads to run simultaneously, each executing a machinelearning model. A priority queue manages the execution of threads, pri-oritizing the most important ones based on the subjective importance ofevents determined by GPT-3.The model incorporates short-term and long-term memory, storinginformation generated at each moment, and uses an Evolutionary Al-gorithm (EA) for training the machine learning models. A preliminaryexperiment was conducted using Python 3.9.12, demonstrating the tech-nical feasibility of the hypothesis. However, limitations such as the lackof a comprehensive environment, absence of load balancing, and GPT-3API constraints were identified.The significance of this study lies in its potential contribution to theunderstanding of consciousness and the development of Artificial GeneralIntelligence (AGI). By exploring the integration of multiple threads ofexecution and machine learning models, this work provides a foundationfor further research and experimentation in the field of computationalconsciousness. Addressing the limitations and potential criticisms willhelp strengthen the model’s validity and contribute to the understandingof this complex phenomenon.
Category: Artificial Intelligence
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