Activation Functions in Neuro Symbolic Integration Using Agent Based Modelling

Logic program and neural networks are two important aspects in artificial intelligence. This paper is part of an endeavour towards neural networks and logic programming integration. The goal in performing logic programming based on the energy minimization scheme is to achieve the best ratio of global minimum. However, there is no guarantee to find the best minimum in the network. To achieve this, activations functions are modified to accelerate the neuro symbolic integration. These activation functions will reduced the complexity of doing logic programming in Hopfield Neural Network (HNN).The activations functions discussed in this paper are new learning rule, Mc Culloch Pitts function and Hyperbolic Tangent Activation function. This paper also focused on agent based modelling for presenting performance of doing logic programming in Hopfield network using various activation functions. The effects of the activation function are analyzed mathematically and compared with the existing method. Computer simulations are carried out by using NETLOGO to validate the effectiveness on the new activation function. The resuls obtained showed that the Hyperbolic Tangent Activation function outperform other activation functions in doing logic programming in Hopfield network. The models developed by agent based modelling also support this theory

Neuro Symbolic Integration and Agent Based Modelling

Logic program and neural networks are two important perspectives in artificial intelligence. The major domain of neuro-symbolic integration is designed by the theory are usually known as deductive systems which less such elements of human reasoning as adaptation, learning and self-organisation. Meanwhile, neural networks, known as a mathematical model of neurons in the human brain, and have various abilities, and moreover, they also provide parallel computations and therefore can perform some calculations quicker than classical learning algorithms. Hopfield network is a feedback (recurrent) neural network, consisting of a set of N interconnected neurons which each neurons are linked to all others in all the directions. It has synaptic strength pattern which involve Lyapunov function E (energy function) for energy minimization events. It operates as content addressable memory systems with binary or bipolar threshold unit

Determine the parameters for photoelectric effect data using correlation and simple linear regression

Pearson's correlation coefficient, otherwise known as the product-moment correlation coefficient, a non-parametric process, is a very important concept in statistics, data science, and even in machine learning. It has gained tremendous acceptance in almost all fields and industries where data analysis is the business of the day. It helps to highlight the affinity between two variables whose behaviour might be entirely different, correlation coefficient is an indicator that shows whether such affinity is positive, negative, or none, when no linear relationship can be established between the variables. It is characterized by a numerical value that ranges between -1 and 1. These values serve as the indicators that determine the status of the relationship. In this research, we utilized the idea of correlation coefficient and simple linear regression on experimental data of photoelectric effects to determine the Planck constant, work function, and threshold frequency using MATLAB code

Prediction of Drug Concentration in Human Bloodstream using AdamsBashforth-Moulton Method

Pharmaceutical drugs are chemicals intended to avoid, assess, heal, or cure a disease. It is also commonly referred to as medication. When medicine is taken, it gets absorbed into the bloodstream, spreads throughout the body, and achieves its maximum concentration. Following this, the medication level gradually decreases as it is removed from the body. The drug concentration according to the time can be predicted using mathematical concepts and pharmacokinetic models. The compartmental model is a fundamental type of model used in pharmacokinetics. The number of compartments required to describe the drug's action in the body is one-compartment, twocompartment, and multicompartment. These models can forecast medication concentrations in the body over time. This paper will focus on the one-compartment model and Adams Bashforth-Moulton method. Adams Method is one of the linear multistep techniques applied to solve numerical ordinary differential equations that contain the predictor method (Adams Bashforth) and corrector method (Adams Moulton). The integrated development environment used for the computation and graphing is MATLAB. The expected result of this report is that we can predict the concentration of the chosen drugs over time and how long a particular person needs to wait before donating blood safely

AGENT BASED MODELLING FOR NEW TECHNIQUE IN NEURO SYMBOLIC INTEGRATION

Logic program and neural networks are two important aspects in artificial intelligence. This paper is part of an endeavour towards neural networks and logic programming integration. The goal in performing logic programming based on the energy minimization scheme is to achieve the best ratio of global minimum. However, there is no guarantee to find the best minimum in the network. To achieve this, a new learning rule based Activation Function was derived to accelerate the performance of doing logic programming in Hopfield Neural Network (HNN). This paper also shows focused on agent based modelling for presenting performance of doing logic programming in Hopfield network using new activation function. The effects of the activation function are analyzed mathematically and compared with the existing method. Computer simulations are carried out by using NETLOGO to validate the effectiveness on the new activation function. The resuls obtained showed that the new activation function outperform the existing method in doing logic programming in Hopfield network. The models developed by agent based modelling also support this theory

Application of Higher Order Hopfield Network

Neural network and logic integration is the latest trend in Artificial Intelligence. Neural Symbolic Integration is a combination of neural networks’ robust learning capabilities with symbolic knowledge representation, reasoning, and explanation capabilities in ways that retain the strengths of each paradigm. In this paper, an Agent Based Modelling (ABM) was introduced by using Netlogo which carry out higher order horn clauses in Hopfield network. Our interest in this paper is confined largely to an important class of neural networks that perform useful computations through a process of learning. So, from the ABM that designed, we can carry out some computer simulation to verify and test the ABM develop

Direct recycling of aluminium 6061 chip through cold compression

Aluminium is globally used in many different ways, from beverages cans to a large aircraft. This is due to aluminium’s remarkable mechanical properties as the same time having a low density. The high usage of aluminium in the everyday life, encourage higher production of aluminium where high energy consumption manufacturing process such as melting bauxite for primary material or melting recycled aluminium conventionally for secondary aluminium. So by directing recycling the aluminium chip through cold compression will reduce the energy consumption during the manufacturing process. In this process the aluminium chipwere compressed at maximum of 45 tons force with a set amount of holding time. The compressed specimens were then strengthening through sintering. Through sintering, the Ultimate tensile strength (UTS) of the specimen increased but at the same time micro-hardness and the density is sacrifice. On the other hand, the Elongation to failure (ETF) reduced due to the high compression force which exceeded the optimum. Further deformation process which involved shearing would be recommended to further improve the overall properties

Using the explicit method to solve parabolic partial differential equations of temperature distribution in the conductor of a crude circuit breaker

In Malaysia, the circuit breaker is one of the most important and essential safety mechanisms in every building. When electricity enters a house, it goes to a circuit breaker box to be divided into a number of circuits. Each of these circuits is protected by a breaker or a fuse. For safety purposes, electrical appliances are designed to keep the current flow at a low level. However, whenever the current flow jumps above the safety level, the circuit breaker does its job by cutting off the circuit. This is done by the conductor’s bending (a bimetallic strip or rod) in the circuit breaker when the wire temperature rises. If the conductor needs to bend upward when heated, the thermal conductivity of the lower metal rod must be higher than that of the upper rod. This paper focuses on the temperature distribution in the conductor of a crude circuit breaker in a fire alarm system by using different materials such as a bimetallic rod. The paper also describes how different materials can affect the efficiency of the fire alarm system. An implicit method makes it possible to tackle such problems. This can be achieved by the development of a simultaneous linear equations’ system for temperature at a specified point in time for the entire interior nodes. Based on the simulation results, it was found that the explicit method (em) is a simple mechanism to solve parabolic partial differential equations (pde). The results can also be improved by using the implicit method to minimize errors