Machine learning is a branch of artificial intelligence that aims at solving real life engineering problems. It provides the opportunity to learn without being explicitly programmed and it is based on the concept of learning from data. It is so much ubiquitously used dozen a times a day that we may not even know it. The advantage of machine learning (ML) methods is that it uses mathematical models, heuristic learning, knowledge acquisitions and decision trees for decision making. Thus, it provides controllability, observability and stability. It updates easily by adding a new patient‘s record.
The application of machine learning models on human disease diagnosis aids medical experts based on the symptoms at an early stage, even though some
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It can be thought as the most appropriate way of mapping a set of input variables with a set of output variables. The system learns to infer a function from a collection of labeled training data. The training dataset contains a set of input features and several instance values for respective features. The predictive performance accuracy of a machine learning algorithm depends on the supervised learning scheme [8]. The aim of the inferred function may be to solve a regression or classification problem. There are several metrics used in the measurement of the learning task like accuracy, sensitivity, specificity, kappa value, area under the curve etc. In this work, the aim is to classify the patients as healthy or ill based on the past medical records. Before solving any engineering problem, it is vital that it is necessary to choose a suitable algorithm for the training purpose based on the type of the data. The selection of a method depends primarily on the type of the data as the field of machine learning is data driven. The next important aspect is the optimization of the chosen machine learning …show more content…
When an algorithm is applied to solve a classification problem with a different set of parameters, the classification accuracy also differs abruptly in each case . The challenge in machine learning to find the most suitable parameter values of the algorithms that solves an engineering problem to the best possible way in terms of performance metrics. Therefore, one has to fine tune the algorithm parameters that best suits the problem. There are several optimization techniques like genetic algorithm, particle swarm optimization , Tabu search methods etc. The focus of the study is to calibrate the algorithm parameters using design of experiment
If a behavior is desirable, consequences called reinforcers are used to encourage the behavior in the future, via the process of reinforcement. Reinforcement can be positive (presenting reinforcing stimulus) or negative (removing a negative stimulus). However, if a behavior is undesired, a negative consequence can be used to discourage the behavior, through the process of either positive or negative punishment. In positive punishment, a negative consequence is presented after the undesired behavior occurs. When negative punishment it used the idea is the same “to discourage future display of undesired behavior,” but instead of presenting a negative stimulus, a desired stimulus is removed following the behavior.
Skinner developed operant conditioning, another style that can explain how people get and manage voluntary behaviors (Hockenbury and Hockenbury, 2014, pg.199). Operant conditioning is the learning development that associates with changing the probability that a response will be done again by shaping the consequences of that response. One likely outcome of a behavior is reinforcement. Reinforcement is a stimulus that increases the behavior to be repeated in the future. There is two types of reinforcement; positive reinforcement and negative reinforcement. Both are processes that increase a particular behavior. Both of this ways can affect future behavior, but they do it in different ways. In operant conditioning positive means adding something and negative means removing something. people can know if positive reinforcement has occur if a reinforcing stimulus makes them more possible to repeat a behavior in a similar situation in the future. According to Hockenbury and Hockenbu...
Computers, like us, need to be taught something. It is necessary to provide the computer with the data needed to learn. Despite the fact that they can run through the data several times quicker than a human, it takes a computer an incredibly long time to learn and is one sided. They are usually capable of knowing how to do one specific task, but at an extreme level. However, with increases to computer speeds and improvements to the code, computers are learning at faster speeds than the first artificial
... middle of paper ... ... In Intelligent Data Engineering and Automated Learning–IDEAL 2006 (pp. 1346-1357. Springer Berlin, Heidelberg.
A case referring to the beneficial use of the expert systems in the health sector was the attempt of the LDS Hospital in Salt Lake city,Utah to build “ the most complex artificial intelligence system ever created'; according to the words of DR David Classen.Its name was AIC or “Antibiotic Computer Consultant'; and it was part of HELP(Health Evaluation through Logical Processing), which was LDS’s hospital information system. The latter was existed, before the implementation of the Expert System.
Positive reinforcement works by presenting something positive to the person after a desired behavior is exhibited, making the behavior more likely to happen in the future (McAdams, 2009). An example of this could be when a child helps their mother with the dishes and the mothers rewards the child with ice cream. Negative reinforcement, is when a behavior is strengthened by stopping, removing or avoiding a negative outcome or aversive stimulus (McAdams, 2009). An example of this could be when the light goes green at a traffic light, the car in front of a person does not move. The person hates when this happens and from experience knows that honking the car’s horn gets cars that are in front of them to go
The expert systems that are created to help in the medical field have been programmed to help in one specific area of medicine (Masci). This is a good strategy to slowly introduce robotics into the medical field. If each robot is specially set up to help with, for example, kidney transplant, the robot would be capable of being an expert and learning how to make these procedures more efficient. Some may argue that robots would be inadequate in the operating room because they may not recognize certain situations. How will we know what these systems can and cannot handle until we try? Scientists now are formulating a plan to program these machines with knowledge that would easily be retrievable. The scientist will word situations in a certain way so the robots can easily identify the information they need to solve the problem (Masci). In an article about this topic, t...
Skinner developed the concept of positive reinforcement which showed how placing a hungry rat in a box. The rat learned to pull the lever so food would be knocked down. Therefore receiving food would prompt them to repeat the action of knocking over the lever. Positive reinforcement strengthens a behavior by providing a consequence an individual finds rewarding. For example, if your mom gives you candy or reward each time you complete your homework it will be more likely to for this behavior to be repeated in the future, therefore strengthening the behavior of completing your homework.
Artificial intelligence(AI) is refer to as computer algorithms that show functions that represent intelligence or duplicate certain components and elements of intelligence (Novella, 2017). Computers are good at crunching numbers, running algorithms, recognizing patterns, and searching and matching data. Artificial intelligence is also defined as the stimulation of human intelligence, functioned or processed by machines, especially computer system (Rouse, 2016). These processes involved learning which means the acquisition of information and the rules for using the information, reasoning whereby using the rules to achieve approximate conclusions, and lastly is self-correction. AI has applications in almost every way we use computers in society (Smith, 2006).
Positive reinforcement is a method of presenting to children the appropriate behavior from the inappropriate behaviors. This is done by pointing out the correct behavior and giving some form of encouraging reward. The idea is that all behaviors
The approach to artificial intelligence should be proceeded with caution. Throughout recent years and even decades before, it has been a technological dream to produce artificial intelligence. From movies, pop culture, and recent technological advancements, there is an obsession with robotics and their ability to perform actions that require human intelligence. Artificial intelligence has become a real and approachable realization today, but should be approached with care and diligence. Humans can create advanced artificial intelligence but should not because of the harm they may cause, the monumental advancement needed in the technology, and that its harm outweighs its benefits.
The advantages of using technology in healthcare are far too many to enumerate. As we become more and more dependent on intelligent machines in the medical field, computation technology, specifically, will have a vital role to play in the coming years. They simplify the design process of medical equipment (like prosthetics, stents, pacemakers, etc.), help simulate the effects of a particular device or drug on the human body, consolidate & manage patient records in a central database, etc. Computers are also living up to the challenge of fulfilling out ever-increasing demands of precision and efficiency.
Negative reinforcement is removing something that is not enjoyable as the result of the behavior that is acceptable example is in Skinners box experiment, a loud noise continuously rang until the rat did what Skinner wanted the rat to do (Cervone, Pervin, Cervone, & Professor of Psychology Lawrence A Pervin, 2013). Positive punishment is used to eliminate a certain behavior and is giving something unenjoyable after the behavior. Negative punishment is used to eliminate a behavior and eliminating something you enjoy after the
Reinforcement theory explains in detail how an individual learns behavior. Managers who are making attempt to motivate the employees must ensure that they do not reward all employees simultaneously. They must tell the employees what they are not doing correct.
Machine learning systems can be categorized according to many different criteria. We will discuss three criteria: Classification on the basis of the underlying learning strategies used, Classification on the basis of the representation of knowledge or skill acquired by the learner and Classification in terms of the application domain of the performance system for which knowledge is acquired.