A Hybrid Moth-Flame Optimization And Extreme Learning Machine Model Of Ib Research

One of the biggest advantages of Neural Network is that it can actually learn from observing data sets. This way it uses a random function approximation tool, which helps to estimate the most efficient and ideal solution while defining all the computing functions and distributions. Neural Networks takes data samples instead of entire data sets to arrive to a solution, which saves a lot of time and money. Neural Networks are considered as simple mathematical models to enhance existing data analysis technology.

Danielsson and De Vries explain how predictions of low probability, worst-case outcomes are extremely poor using Value at Risk. These, importantly, are the most influential events that deserve the most attention. Such a blind spot is unacceptable. As such, they offer an alternative: an extreme value estimator. When tested against VaR and historical simulations far out in the tails, it performs much better in locating the depths of a negative shock. It charts tails according to particular parametric distributions, “develop[ing] a straightforward rule for obtaining multi-period VaR from the single period VaR.” It is still fraught with several problems, but makes much more realistic assumptions about the distribution of stock market returns. Moreover, it more closely mimics the actual returns of the market.

Mental fortitude starts very early in life, even moments inside the womb of an expecting mother can be used constructively for the maturation of her child. Time spent reading stories, singing songs, and speaking jump starts the fetus's brain development. New born babies can recognize the voices of their parents before birth. The fetus of a child can sense stress levels in the mother while still within the womb. As an infant matures into early childhood Teachers, parents, and caregivers can facilitate safe self-awareness and positive esteem building environments. Places like the Kid Space Museum in Pasadena, California allows for children to play and learn in safe environments. Kid space defines their operation as “An incredible seed bed of learning and discovery, the purpose of a childcare’s museum is to inspire children to learn and grow, equip parents and teachers, and to be a safe place where learners of all abilities can do so at their own pace and focus on topics of interest to them. This is often referred to as “Free Range Learning”.

My middle school self spent her evenings in front of a music stand for which she was already too tall. I would dive into that week’s clarinet etude, punctuating the tune with squeaks and wrong notes, still full—mentally and physically—from the dinner table offerings, food and academic discussion. My parents expected my sounds only to improve. But then, in grade ten, I attacked multiphonics, playing more than one note at a time, and erased that hope for improvement. To make up for my inability to master the technique, I became an expert on the theory, which surprisingly contained mathematical concepts. Two notes that sound good together have very simple ratios of frequencies. The notes I’d play together probably had ratios somewhere around 453/920. But I liked the theory more anyway. The noises made the math real. I started listening for matching frequencies, and would agitate my band friends by blurting out fractions in the middle of songs.

Financial markets are intertwined into the lives of everybody today. These markets set prices for food, gas, and various other items that people use often. There are even public cable television channels, such as CNBC, that exclusively cover financial markets. The worlds current financial events such as the US debt crisis, and the Federal Reserves’ quantitative easing have caused a rise in the public’s awareness of the financial markets. One of the most feared events in financial markets is a crash. A crash is a very steep drop in price of all securities in that market. Some recent crash events are the financial crisis of 2008, flash crash of May 2010, and August 2011. Flash crashes are a relatively new phenomenon to the markets, and these recent, unprecedented events have brought controversy to one of the newest forms of trading called High Frequency Trading(HFT). HFT is the act of executing millions of dollars worth of trades at sub second speed according to predictive software, or statistical models and algorithms. HFT allows hedge funds, and other types of financial companies the ability to trade in multiple markets faster than ever before. This incredibly broad and super fast ability to trade is one of the main reasons HFT has come under fire as a detrimental form of trading. Some of the controversy that HFT experiences consists of possible involvement in flash crashes and added

What did I learn in this class? It would be truly impossible for me to talk about one thing which I learned in this class, because I had not had any Artificial Intelligence experience or class before, so everything covered in class was a learning experience for me, to include JAVA programming language. Within this paper, I will talk about not only what I learned in class, but also what I found to be interesting and what I will probably use in the future. The topics I feel I learned the most about were Genetic Algorithms (GAs) and Neural Networks (NNs). A GA is a learning model that owes its performance to a metaphor of some of the mechanisms of evolution observed in nature (such as sexual reproduction and the principle of

Differential Evolution, or DE, is a cost minimisation method that utilises various evolutionary algorithm concepts, but can also handle non-differentiable, nonlinear, and multimodal objective functions that standard evolutionary algorithms cannot. Experiments have shown that DE shows good convergence properties and outperforms other EA’s, converging faster and with more certainty than many other popular global optimization methods.

Multilayer Perceptron (MLP) is an artificial neural network that learns nonlinear function mappings. An MLP can be viewed as a logistic regression classifier where the input is first transformed using a learned non-linear transformation. This transformation projects the input data into space where it becomes linearly separable. This intermediate layer is referred to as a hidden layer. A single hidden layer is sufficient to make MLPs a universal approximator.

This dissertation consists of seven chapters. The first chapter provides a general introduction and overview of the area of research, including a general introduction of 0-1 knapsack problem, Teaching learning based optimization (TLBO) and problem definition. The motivation is discussed along with the scope of the study and

Relatively accurate prediction of multi-tiered, non-linear events has long been a difficult and time-consuming task to perform; forecasting the movement of securities on the stock market included. Stock prices fluctuate for innumerable reasons, so correctly forecasting a stock’s movement can be extremely difficult. There are two areas that have massive effect on stock pricing: the psychology, or sentiment of investors and the mathematical, or analytical standpoint. In order to effectively predict stock price movement, these two areas, more than anything else, must be factored in. Fundamental and technical analysis are easily included, but accounting for investor sentiment is more complicated. Using artificial neural networks (ANNs) coupled with the two schools of traditional analysis and regression analysis could prove useful in correctly forecasting the price movement of stocks on any exchange. How can ANNs be implemented with regression analysis to accurately predict the movement of stock prices? This report will be a semi-technical feasibility analysis of the different types of ANNs, predictive algorithms and financial analysis

Time series forecasting plays an important role in the academic and practical domains. Many researchers have studied on this area from several years. There are many models which are used to improve the accuracy of time series forecasting. In this paper, I have focused on one method i.e. Neural Networks. In the first section of the report, I will give brief introduction on time series forecasting and neural networks. In the next part, I will explain this neural method which is used for forecasting in the literature review. At last, I will conclude the paper. Moreover, the main aim of this paper is to define the neural network method among the different methods in the time series forecasting.

The aim of this research paper is to facilitate prediction of the closing price of a particular stock for a given day. A thorough analysis of the existing models for stock market behavior and different techniques to predict stock prices was carried out. These included the renowned Efficient Market Hypothesis and its rival, the Chaos Theory. It was found that the Chaos Theory is the best model for modeling the behavior of a stock market. Chaos is a nonlinear process which appears to be random, i.e. there is an order-disorder relation between

In this chapter, we introduce the concept of market timing, and discuss various market timing models in the literature. According to Admati, Bhattacharya, Pfleiderer and Ross (1986), the superior performance of an investment is due to either the manager’s selection ability or timing ability or the combination of the two abilities. Marketing timing is a type of dynamic asset allocation strategy that adjusts a portfolio’s market exposure that is based on the manager’s forecast about the market (Admati et al., 1986; Chen, 2007; Chen and Liang, 2007). Therefore, the managers who have successful timing ability can increase portfolios’ market exposure before a market rise and decrease portfolios’ market exposure prior to a market fall.

Embedded method: Embedded method is a combination of wrapper method and embedded method. This decreases the computational cost than wrapper approach and captures feature dependencies. It searches locally for features that allow better discrimination and also the relationship between the input feature and the targeted feature. It involves the learning algorithm which is used to select optimal subset among

Abstract— Genetic Algorithm (GA) is a stochastic randomized blind search and optimization technique based on evolutionary computing that has already been proved to be robust and effective from its outcome in solving problems from variety of application domains. Clustering is a vital technique to extract meaningful and hidden information from the datasets. Clustering techniques have a broad field of application including bioinformatics, image processing and data mining. In order to the find the close association between the densities of data points, in the given dataset of pixels of an image, clustering provides an easy analysis and proper validation. In this paper, we propose an evolutionary computing based approach for unsupervised image clustering using elitist GA (EGA) – a efficient variant of GA that segments an image into its constituent parts automatically. The aim of this algorithm is to produce precise segmentation of images using intensity information along with their neighbourhood relationships. Experimental results from simulation study reveal that the algorithm generates good quality segmented image.