Transactions on Machine Learning and Artificial Intelligence <p>Transactions on Machine Learning and Artificial Intelligence is peer-reviewed open access online journal that provides a medium of the rapid publication of original research papers, review articles, book reviews and short communications covering all areas of machine learning and artificial Intelligence. The journal publishes state-of-the-art research reports and critical evaluations of applications, techniques and algorithms in machine learning, artificial intelligence, cognitive science, software engineering, database systems, soft computing, optimization and modelling and related application areas.</p> en-US Transactions on Machine Learning and Artificial Intelligence 2054-7390 Ten Artificial Human Optimization Algorithms <p>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 where as 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. &nbsp;&nbsp;&nbsp;</p> Satish Gajawada Hassan Mustafa Copyright (c) 2019 Transactions on Machine Learning and Artificial Intelligence 2019-07-01 2019-07-01 7 3 01 16 10.14738/tmlai.73.6631 Two Factor Authentication Framework Using OTP-SMS Based on Blockchain <p>The authentication process is the main step which should be used to confirm that the user is the legitimate one and give the access only for him. Recently, Two Factor Authentication (2FA) schemes have been used by most of the applications to add an extra layer of security on the login process and solve the vulnerabilities of using only one factor for authentication. OTP-SMS is one of the most common methods which has been used in 2FA. However, attackers found a way to attack this method and gain an access to the user’s account without their permission. In this paper, we proposed a new 2FA framework for OTP-SMS method to prevent different attacks, mainly Man In The Middle (MITM) attack and third party attack. The proposed framework is based on the use of Blockchain technology, which add more security and better environment for authentication process. The proposed framework uses an encrypted OTP, which generated by smart contract and uses also its hash value to send it to the application/website to complete the authentication process. We introduced a comparison between our proposed framework and other two frameworks which uses Blockchain to secure OTP-SMS. Our framework found to be secure against MITM and third party attacks and the computation time and complexity are less than other frameworks.</p> Eman Alharbi Daniyal Alghazzawi Copyright (c) 2019 Transactions on Machine Learning and Artificial Intelligence 2019-07-01 2019-07-01 7 3 17 27 10.14738/tmlai.73.6524 On Finding Geodesic Equation of Two Parameters Binomial Distribution <p><strong>&nbsp;</strong>The purpose of this paper is to find a general form of the geodesic equation of the binomial distribution. Using Darboux’s theory we will set up a second order partial differential equation. Then we will apply the chain rule to transform the variable and rotate the axis to remove the interaction term, which will lead us to find the geodesic equation of binomial distribution. To illustrate how we can find such a geodesic equation in practice, we demonstrate by an example.</p> William W.S. Chen Copyright (c) 2019 Transactions on Machine Learning and Artificial Intelligence 2019-07-01 2019-07-01 7 3 28 34 10.14738/tmlai.73.6696